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  1. [img]https://i116.fastpic.org/big/2021/1028/98/df1bc72551347e353f5f85f3ce816798.jpeg[/img] Last Update: 10/2021 Duration: 2h7m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 753 MB Genre: eLearning | Language: English Deep Neural Networks, Convolutional Neural Networks, Object Detection, Computer Vision, LSTM, Tensor Flow Certification What you'll learn: Neural Network Basics, Multi Layered Perceptron, Convolutional Neural Networks Object Detection, Computer Vision Practical applications of Deep Neural Networks, Real world case studies Tensor flow Beginner to Professional and Tensor flow Certification Requirements: No programming experience required Description: This course not only simplifies complex theoretical Deep Learning concepts but also teaches to solve real world problems using Deep Neural Networks. There are sufficient number of Real world Projects discussed in this course in order to make learner Job ready. The important aspect of this course is to prepare learner for Google Tensor Flow Certification Examination. Apart from Deep Neural fundamentals here we discuss Convolutional Neural Networks, Long-short term memory(LSTM), Generative Adversarial Networks (GANs), Encoder Decoder Models, Attention Models, Image Segmentation. This course also teaches Google Tensor Flow from a beginners stand point. One of the main aim of this course is to make learner a professional in Tensor Flow. Case studies like Self Driving Car have been discussed in great detail. After taking this course the learner will be expert in following topics. a) Theoretical Deep Learning Concepts. b) Convolutional Neural Networks c) Long-short term memory d) Generative Adversarial Networks e) Encoder- Decoder Models f) Attention Models g) Object detection h) Image Segmentation i) Transfer Learning j) Open CV using Python k) Building and deploying Deep Neural Networks l) Professional Google Tensor Flow developer m) Using Google Colab for writing Deep Learning code n) Python programming for Deep Neural Networks The Learners are advised to practice the Tensor Flow code as they watch the videos on Programming from this course. Who this course is for: For everyone who want to Learn in-depth Theoretical and Practical Deep Learning and its Real world applications Homepage [code]https://www.udemy.com/course/deep-learning-real-world-projects-tensorflow-certification/[/code] [code] https://hot4share.com/548zjg8dqdxg/mkwrd.D.L.R.W.P..T.C.part1.rar.html https://hot4share.com/m5tyt7mibv1f/mkwrd.D.L.R.W.P..T.C.part2.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/bA0722a8da1ced9b/mkwrd.D.L.R.W.P..T.C.part1.rar https://uploadgig.com/file/download/7880a468a1797edF/mkwrd.D.L.R.W.P..T.C.part2.rar Download ( Rapidgator ) https://rapidgator.net/file/5a433241fa9e22cc018d98d3d458cfbb/mkwrd.D.L.R.W.P..T.C.part1.rar.html https://rapidgator.net/file/9828bfcdc24d5ef045ac9ede7106f272/mkwrd.D.L.R.W.P..T.C.part2.rar.html[/code] [b]Links are Interchangeable - No Password - Single Extraction[/b]
  2. [img]https://i116.fastpic.org/big/2021/1024/bc/2f804fdbcd93a6b35e77ca9eaec586bc.jpeg[/img] MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 25 lectures (3h 45m) | Size: 1.59 GB How Agents Can Learn In Environments With No Rewards [b]What you'll learn:[/b] How to Code A3C Agents How to Do Parallel Processing in Python How to Implement Deep Reinforcement Learning Papers How to Code the Intrinsic Curiosity Module [b]Requirements[/b] Experience in coding actor critic agents [b]Description[/b] If reinforcement learning is to serve as a viable path to artificial general intelligence, it must learn to cope with environments with sparse or totally absent rewards. Most real life systems provided rewards that only occur after many time steps, leaving the agent with little information to build a successful policy on. Curiosity based reinforcement learning solves this problem by giving the agent an innate sense of curiosity about its world, enabling it to explore and learn successful policies for navigating the world. In this advanced course on deep reinforcement learning, motivated students will learn how to implement cutting edge artificial intelligence research papers from scratch. This is a fast paced course for those that are experienced in coding up actor critic agents on their own. We'll code up two papers in this course, using the popular PyTorch framework. The first paper covers asynchronous methods for deep reinforcement learning; also known as the popular asynchronous advantage actor critic algorithm (A3C). Here students will discover a new framework for learning that doesn't require a GPU. We will learn how to implement multithreading in Python and use that to train multiple actor critic agents in parallel. We will go beyond the basic implementation from the paper and implement a recent improvement to reinforcement learning known as generalized advantage estimation. We will test our agents in the Pong environment from the Open AI Gym's Atari library, and achieve nearly world class performance in just a few hours. From there, we move on to the heart of the course: learning in environments with sparse or totally absent rewards. This new paradigm leverages the agent's curiosity about the environment as an intrinsic reward that motivates the agent to explore and learn generalizable skills. We'll implement the intrinsic curiosity module (ICM), which is a bolt-on module for any deep reinforcement learning algorithm. We will train and test our agent in an maze like environment that only yields rewards when the agent reaches the objective. A clear performance gain over the vanilla A3C algorithm will be demonstrated, conclusively showing the power of curiosity driven deep reinforcement learning. Please keep in mind this is a fast paced course for motivated and advanced students. There will be only a very brief review of the fundamental concepts of reinforcement learning and actor critic methods, and from there we will jump right into reading and implementing papers. The beauty of both the ICM and asynchronous methods is that these paradigms can be applied to nearly any other reinforcement learning algorithm. Both are highly adaptable and can be plugged in with little modification to algorithms like proximal policy optimization, soft actor critic, or deep Q learning. Students will learn how to: Implement deep reinforcement learning papers Leverage multi core CPUs with parallel processing in Python Code the A3C algorithm from scratch Code the ICM from first principles Code generalized advantage estimation Modify the Open AI Gym Atari Library Write extensible modular code This course is launching with the PyTorch implementation, with a Tensorflow 2 version coming. [b]Who this course is for[/b] This course is for advanced students of deep reinforcement learning Homepage [code]https://www.udemy.com/course/curiosity-driven-deep-reinforcement-learning/[/code] [code] https://hot4share.com/3mm35f37rfux/4uor9.C.D.D.R.L.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/ba4dfdCb4a303bB7/4uor9.C.D.D.R.L.rar Download ( Rapidgator ) https://rapidgator.net/file/d49580424d701c02207e184fa228ebeb/4uor9.C.D.D.R.L.rar.html[/code] [b]Links are Interchangeable - No Password - Single Extraction[/b]
  3. [img]https://i116.fastpic.org/big/2021/1018/5c/f428af2c46f99e930db02aaf254e085c.jpg[/img] [b]Wonders of the Celtic Deep S01 iP WEBRip x264-ION10[/b] - "N/A" [b]Genres:[/b] Documentary [b]Stars:[/b] Siân Phillips [b]Rating:[/b] 0.0/10 [code]File:..............Wonders of the Celtic Deep S01E01 WEBRip x264-ION10.mp4 Size:..............2.21 GB Format:............mp4 Resolution:........720x400, 50.000 fps BitRate:...........1100 Kbps Duration:..........58min 53s Audio:.............AAC, 48 Khz, 2 channels, 256 Kbps, English iMDb:..............https://www.themoviedb.org/tv/136333[/code] [color=red][b]Premium Single Download Link: [NO RAR][/b][/color] [code] https://rapidgator.net/file/e5f086267dce02cdd01ca4dc92742840/2_English.srt https://rapidgator.net/file/e5f086267dce02cdd01ca4dc92742840/2_English.srt https://rapidgator.net/file/e5f086267dce02cdd01ca4dc92742840/2_English.srt https://rapidgator.net/file/e5f086267dce02cdd01ca4dc92742840/2_English.srt https://rapidgator.net/file/56874c1ce84ca90b81882db3fc4ef88c/Wonders.of.the.Celtic.Deep.S01E01.WEBRip.x264-ION10.mp4 https://rapidgator.net/file/6d25fc58d1abfd7ca9aaf6dbaab7fcc1/Wonders.of.the.Celtic.Deep.S01E02.WEBRip.x264-ION10.mp4 https://rapidgator.net/file/ee396e8d8bb43de53b490b4551204c4e/Wonders.of.the.Celtic.Deep.S01E03.WEBRip.x264-ION10.mp4 https://rapidgator.net/file/6025ae239b9a32a710e96668f4692534/Wonders.of.the.Celtic.Deep.S01E04.WEBRip.x264-ION10.mp4 [/code] [code]Free Download Links: https://rapidgator.net/file/30b8ebf79b8b91ecdf90c1a9fa1f01c6/Wonders.of.the.Celtic.Deep.S01.iP.WEBRip.x264-ION10.part1.rar https://rapidgator.net/file/3b46e8d2c5a4ea3b3d7ed5ca00159d79/Wonders.of.the.Celtic.Deep.S01.iP.WEBRip.x264-ION10.part2.rar https://rapidgator.net/file/60d727d3f5ab5a5ca408a6b3948fcf94/Wonders.of.the.Celtic.Deep.S01.iP.WEBRip.x264-ION10.part3.rar [/code] [img]https://i.postimg.cc/zXfxvvxt/Support-textsashen2016.png[/img] [img]https://i.postimg.cc/76pRg3LC/Wonders-of-the-Celtic-Deep-S01-i-P-WEBRip-x264-ION10.jpg[/img]
  4. [img]https://i115.fastpic.org/big/2021/1003/2c/9620b827dc849f35a48546322d88952c.jpeg[/img] Created by Noble Arya | Last updated 9/2021 Duration: 55m | 4 sections | 7 lectures | Video: 1280x720, 44 KHz | 868 MB Genre: eLearning | Language: English + Sub Mindfulness Practitioner Certification (Gold Level) Meditation [b]What you'll learn[/b] How to learn deep attention and focus management (Attention is your greatest asset in the world) How to Learn deep Focus and achieve any goal of life. (Whatever you focus on will grow, Focus is most valuable asset in the world) How to Practice 360 Mindfulness (Deep Single Pointedness Focus) How to apply 360 Mindfulness end to end life for true freedom How to get up to 10X Growth in life by applying 360 Mindfulness daily [b]Requirements[/b] No or Prerequisites are required for this course [b]Description[/b] Mindfulness Practitioner Certification (Gold Level Certification) Meditation \n Hello, I'm Noble. Let me share my journey as Global Future Skills & Computer Science - Artificial Intelligence Expert. We have Served 4000+ Students and 500+ Teachers. I have done Global Future Skills Implementation and Future Skills Research for last 15 Years. I am Lifelong Lerner of Future Skills, Future Technologies. I am Self Taught Computer - Artificial Intelligence Scientist and Super Pure Consciousness Expert. \n Value Spent on Skills: I have Spent 75000+ USD on 1. Digital Skills 2. Future Skills 3. Freedom and Transformation Skills 4.Technologies Skills 5.Soft Skills and I have done 500+ global digital and future skills trainings. \n I have done 100+ Project with these Skills with over 15 years: 1. GE. 2. Wipro Technologies. 3. Himalayan Institute of Alternatives Ladakh. 4. Teach for India. 5. Harvard Medical School. 6. Toastmasters International. \n Skills Research: Digital and Future Skills Research for last 15 Years and also have done Implementation real projects. I am Lifelong Learner of all thses skills. \n Skills Training Conducted: I have been conducting trainings for 7 years. I have been training professionals how to file Ideas, Kaizen, Innovative Ideas, Creativity, and Problem-Solving skills. I have been training CEOs of Fortune 500 companies, Contract Managers, Automobile Project Managers, Project Managers Executives, Teachers, Students, Collage Students, and Universities on Future Skills, Project Management, Future Technologies, Total Quality Management I do Running training daily as an athlete, I have been participating in WIPRO Runs and other marathons for the last 10 years Deep Pursuits: I have been doing deep Meditation ( have done 9 courses (10 days) & (served 10 days courses) from 2016. I have been Reading Business Magazines (Harvard Business Review). I have been Reading Management Books like Blue Ocean Strategy, Emotional intelligence, Lords of Strategy, Culturomics, Behavioural Economics and Think Fast and slow. Proactive Learner & Researcher of Future Technology: Cloud Computing Digital Media Big Data Stream Further Natural Interaction Humanoid Quantum Computing Hyper Automation Artificial Intelligence Proactive Learning Future New Technology Computer Architecture\n [code] https://hot4share.com/vw0vxknh7fwz/21esm.360.Mindfulness.Deep.Single.Pointedness.Focus.and.Freedom.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/1Eb3f3117e563fAe/21esm.360.Mindfulness.Deep.Single.Pointedness.Focus.and.Freedom.rar Download ( Rapidgator ) https://rapidgator.net/file/a0c7856dbb5bd5c0b04f80dc6b50c127/21esm.360.Mindfulness.Deep.Single.Pointedness.Focus.and.Freedom.rar.html[/code] [b]Links are Interchangeable - No Password - Single Extraction[/b]
  5. [img]https://i115.fastpic.org/big/2021/0928/6f/0f109b4596f1f13b386da9053876c36f.jpeg[/img] MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 19 lectures (3h 50m) | Size: 2 GB Fake News Detection [b]What you'll learn:[/b] Deep Learning for Natural Language Processing Fake news detection Knowledge based fake news detection Style based fake news detection Propagation based fake news detection Credibility based fake news detection DL for NLP [b]Requirements[/b] Basics of machine learning Basic understanding of deep learning models [b]Description[/b] Fake news is now viewed as one of the greatest threats to democracy, journalism, and freedom of expression. The reach of fake news was best highlighted during the critical months of the 2016 U.S. presidential election campaign. During that period, the top twenty frequently-discussed fake election stories generated 8.7M shares, reactions, and comments on Facebook, ironically, more than the 7.4M for the top twenty most-discussed election stories posted by 19 major news websites. Research has shown that compared to the truth, fake news on Twitter is typically retweeted by many more users and spreads far more rapidly, especially for political news. Our economies are not immune to the spread of fake news either, with fake news being connected to stock market fluctuations and large trades. For example, fake news claiming that Barack Obama, the 44th President of the United States, was injured in an explosion wiped out $130 billion in stock value in 2017. These events and losses have motivated fake news research and sparked the discussion around fake news, as observed by skyrocketing usage of terms such as "post-truth" - selected as the international word of the year by Oxford Dictionaries in 2016. The many perspectives on what fake news is, what characteristics and nature fake news or those who disseminate it share, and how fake news can be detected motivate the need for a comprehensive introduction and in-depth analysis, which this course aims to develop. This course is divided into three sections. In the first section, I will introduce fake new detection, and discuss topics like "what is fake news and related areas", "how to manually identify fake news", "why detect fake news" and "efforts by various organisations towards fighting fake news". In the second section we will focus on various types of fake news detection methods. Specifically I will talk about four different fake news detection methods which are knowledge based fake news detection, style based fake news detection, propagation based fake news detection and credibility based fake news detection. Lastly in the third section I'll talk about other perspectives and topics related to fake news detection including fake news detection datasets, explainable fake news detection, concerns around fake news detection and research opportunities. Hope you will enjoy this course and find the ideas useful for your work. [b]Who this course is for[/b] Beginners in deep learning BTech and Masters students who have done a basic course in deep learning Social science students with an inclination towards data science Python developers interested in data science concepts Masters or PhD students who wish to learn deep learning concepts quickly Deep learning engineers and developers Employees of Social media companies Homepage [code]https://www.udemy.com/course/ahol-dl4nlp10/[/code] [code] https://hot4share.com/qzywiqp6ly0r/ka97b.Deep.Learning.for.NLP..Part.10.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/38cb0437f12be622/ka97b.Deep.Learning.for.NLP..Part.10.rar Download ( Rapidgator ) https://rapidgator.net/file/68c0bdcc4ed62059bc518815f62accbf/ka97b.Deep.Learning.for.NLP..Part.10.rar.html[/code] [b]Links are Interchangeable - No Password - Single Extraction[/b]
  6. [img]https://i115.fastpic.org/big/2021/0924/c8/2b7ec0d7ec7c053412e737092f98bac8.jpeg[/img] Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 2.21 GB | Duration: 4h 31m The most comprehensive course on the DNS protocol What you'll learn Configure authoritative name servers, cache only servers and resolvers Troubleshoot DNS issues Choose, register and manage your own domain names Secure your DNS infrastructure Analyse the DNS protocol Construct your own DNS packets from scratch Description Who is this course for This course is mainly for network engineers, IT technicians, system administrators, cyber security professionals, computer science students and anybody who is interested in starting a career in the IT industry. What you will learn Configure authoritative name servers, cache only servers and resolvers Troubleshoot DNS issues Choose, register and manage your own domain names Secure your DNS infrastructure Analyse the DNS protocol Construct your own DNS packets from scratch Prerequisites Basic IT Skills A fundamental understanding of the TCP/IP suite A good knowledge of the Linux operating system A computer system with ideally 16GB of RAM and 200GB of disk space FAQs Do I need any previous experience? Any previous experience in networking, system administration and computer systems will definitely be helpful though not necessary. Do I get any support with this course? Yes, any question you have will be answered by email within 24 hours. I am an experienced network engineer with previous experience in DNS. How do I benefit from this course? The knowledge contained in this course will definitely enhance your existing understanding of the DNS protocol. What's more, the custom scripts, cheat sheets and configuration guides included in the course will help you in your day-to-day work. Will I get any resources with this course? Yes, you will get an assortment of resources in the form of scripts, configuration guides, cheatsheets and infographics. Will I get a certificate of completion at the end? Yes, you will indeed! Who this course is for: This course is mainly for network engineers, IT technicians, system administrators, cyber security professionals, computer science students and anybody who is interested in starting a career in the IT industry. Homepage [code]https://www.udemy.com/course/dns-deep-dive/[/code] [code] https://hot4share.com/7glv7r9opru3/zyanb.Udemy..DNS.Deep.Dive.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/Ac6c7a06B518C013/zyanb.Udemy..DNS.Deep.Dive.rar Download ( Rapidgator ) https://rapidgator.net/file/5bbaae83e780a9bc0a76851bfa045631/zyanb.Udemy..DNS.Deep.Dive.rar.html[/code] [b]Links are Interchangeable - No Password - Single Extraction[/b]
  7. [img]https://i115.fastpic.org/big/2021/0924/34/fef421001d0d5f8fe57f9b9b38582734.jpeg[/img] MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 17 lectures (3h 4m) | Size: 1.1 GB Deep Dive into the Deep and Dark Web [b]What you'll learn:[/b] Understand how the TOR Network works. How to run a .ONION website. How to Operate and Run a TOR Relay. How Protect Yourself in the DeepWEB. Where to find DeepWeb Links. [b]Requirements[/b] Basic to Mid-Level undestanding of Computer Networks and Operating Systems. [b]Description[/b] In this course the student will learn about a Deep Web which is an unindexed part of the internet. Understand what are the differences between the traditional web and demystify every subject that is often treated in a generalized way. The intention of the course is to give clarity and depth to the topic. At the end of the course, the student will know what the Deep Web is and how it works, understanding its risks and even how to host a website on this network or operate a TOR Relay. This course is designed for people in a hurry to understand the Deep Web/Dark Web. Know that buying this course feels like you hired a cybersecurity professional and received a 3-hour lecture covering the main topics of the Deep Web. The course has an introduction to make a leveling and allow a better use of all. After the introduction, the focus on Hands-ON is aimed at demonstrating and allowing the student to follow the entire course and repeat the process for fixing the content. The classes are all guided with the student watching the instructor who is doing demonstrations and commenting on each step that is demonstrated. Hope you enjoy this course! [b]Who this course is for[/b] Law Enforcement Personnel Journalists Lawyers Anyone who is curious about the Deep and DarkWeb Homepage [code]https://www.udemy.com/course/deep-web-crash-course/[/code] [code] https://hot4share.com/dqhpcta5qhyy/2gbvk.Deep.Web..Crash.Course.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/8E0686276518a28f/2gbvk.Deep.Web..Crash.Course.rar Download ( Rapidgator ) https://rapidgator.net/file/f223d17035507f7a2bb25c35e24e4268/2gbvk.Deep.Web..Crash.Course.rar.html[/code] [b]Links are Interchangeable - No Password - Single Extraction[/b]
  8. [img]https://i115.fastpic.org/big/2021/0917/dc/d065869829b7d33991d20e8a6d4141dc.jpeg[/img] MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 12 lectures (2h 15m) | Size: 1.1 GB Hate Speech Detection [b]What you'll learn:[/b] Deep Learning for Natural Language Processing Hate Speech Detection DL for Hate Speech Detection Multimodal Hate Speech Detection Analysis of hate speech detection results DL for NLP [b]Requirements[/b] Basics of machine learning Basic understanding of deep learning models [b]Description[/b] Since the proliferation of social media usage, hate speech has become a major crisis. On the one hand, hateful content creates an unsafe environment for certain members of our society. On the other hand, in-person moderation of hate speech causes distress to content moderators. Additionally, it is not just the presence of hate speech in isolation but its ability to dissipate quickly, where early detection and intervention can be most effective. Through this course, we will provide a holistic view of hate speech detection mechanisms explored so far. In this course, I will start by talking about why studying hate speech detection is very important. I will then talk about a collection of many hate speech datasets. We will discuss the different forms of hate labels that such datasets incorporate, their sizes and sources. Next, we will talk about feature based and traditional machine learning methods for hate speech detection. More recently since 2017, deep learning methods have been proposed for hate speech detection. Hence, we will talk about traditional deep learning methods. Next, we will talk about deep learning methods focusing on specific aspects of hate speech detection like multi-label aspect, training data bias, using metadata, data augmentation, and handling adversarial attacks. After this, we will talk about multimodal hate speech detection mechanisms to handle image, text and network based inputs. We will discuss various ways of mode fusion. Next, we will talk about possible ways of building interpretations over predictions from a deep learning based hate speech detection model. Finally, we will talk about challenges and limitations of current hate speech detection models. We will conclude the course with a brief summary. [b]Who this course is for[/b] Beginners in deep learning Social science students with an inclination towards data science Humanities students Python developers interested in data science concepts Masters or PhD students who wish to learn deep learning concepts quickly Deep learning engineers and developers Employees of Social media companies Homepage [code]https://www.udemy.com/course/ahol-dl4nlp9/[/code] [code] https://hot4share.com/7c5mtu3ownu0/hq8ks.Deep.Learning.for.NLP..Part.9.part1.rar.html https://hot4share.com/3qrz0xdpzb26/hq8ks.Deep.Learning.for.NLP..Part.9.part2.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/31d8144f348b29f2/hq8ks.Deep.Learning.for.NLP..Part.9.part1.rar https://uploadgig.com/file/download/05181e50d43F2fa5/hq8ks.Deep.Learning.for.NLP..Part.9.part2.rar Download ( Rapidgator ) https://rapidgator.net/file/a9e30aab4af7b1156edd812fa6b370b0/hq8ks.Deep.Learning.for.NLP..Part.9.part1.rar.html https://rapidgator.net/file/44eee873d8e8ce21e2e8610933da6a56/hq8ks.Deep.Learning.for.NLP..Part.9.part2.rar.html[/code] [b]Links are Interchangeable - No Password - Single Extraction[/b]
  9. [img]https://i115.fastpic.org/big/2021/0912/e7/596f5807672007686ae875c82f8e4de7.jpeg[/img] MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 30 lectures (1h 23m) | Size: 449.1 MB Re-explore containers from open standards perspective [b]What you'll learn:[/b] Understanding of Open Container Standards Hands-on experience with new container tools Insights how containers work from inside Realisation that Docker is just one of container tools [b]Requirements[/b] Basic Linux knowledge. Some Docker experience will also be helpful. [b]Description[/b] When someone wants to say "tissue", quite often they would say "Kleenex". When someone wants to say "containers", most likely they would say "Docker" instead. We realise that "Kleenex" is just a widely accepted name for a particular type of a paper product. If we will see a tissue from another brand, we won't get confused, we will still know what to do with it. But is that the same with Docker? What do we mean by saying "Docker container"? Is it some generic container or is it something Docker specific? What about the "Docker image"? Do we know what the real tissue behind this is? We've all been using Docker for so long that we stopped thinking "container" and instead we think "Docker". In the Dockerless course you will learn to see beyond Docker - and try out a lot of new tools. We'll talk about open container standards and investigate them on practice by using half a dozen various container tools; We will build container images and run containers - all without Docker; We will also learn why you don't need a container image to run a container, and why many big players, including AWS, RedHat and Google, move away from Docker - and what they rely on instead; [b]Who this course is for[/b] Developers and infrastructure engineers who want to better understand how Linux containers work beyond just Docker. Homepage [code]https://www.udemy.com/course/dockerless/[/code] [code] https://hot4share.com/mn7f95ru526o/yzo7h.Dockerless.Deep.Dive.Into.What.Containers.Really.are.About.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/17Bd8b5ab31844eE/yzo7h.Dockerless.Deep.Dive.Into.What.Containers.Really.are.About.rar Download ( Rapidgator ) https://rapidgator.net/file/9da1a0828c930ab9da7603e9f7dd30d2/yzo7h.Dockerless.Deep.Dive.Into.What.Containers.Really.are.About.rar.html ++++++++++++++++++++++++++ https://ddownload.com/zg20ijgnc8v8/yzo7h.Dockerless.Deep.Dive.Into.What.Containers.Really.are.About.rar[/code] [b]Links are Interchangeable - No Password - Single Extraction[/b]
  10. [img]https://i115.fastpic.org/big/2021/0907/d8/354bb573cc85a6a08cb8979fbb6a50d8.jpeg[/img] MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 13 Lessons (54m) | Size: 474.1 MB This is the perfect place to master modeling tools in Blender. These skills are necessary for anyone looking to pursue a 3D career. This course will cover skills traditionally used in animation, motion design, and video game design. We will be going through the modeling techniques to model characters like this while learning the modeling workflow in Blender. You'll learn the tools, what it means to have good topology, and tricks to speed up your workflow. Feel free to follow along with the class example or make your own! Homepage [code]https://www.skillshare.com/classes/Blender-3D-Deep-Dive-Into-Modeling-With-Blender-3D/1537563083[/code] [code] https://hot4share.com/n61h43wnzjsu/mj1a3.Blender.3D.Deep.Dive.Into.Modeling.With.Blender.3D.rar.html ++++++++++++++++++++++++++ https://ddownload.com/u0ern7rwd1k3/mj1a3.Blender.3D.Deep.Dive.Into.Modeling.With.Blender.3D.rar [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/2B94bEEe13952277/mj1a3.Blender.3D.Deep.Dive.Into.Modeling.With.Blender.3D.rar Download ( Rapidgator ) https://rapidgator.net/file/671a62669f72f0d88d96a477f062c138/mj1a3.Blender.3D.Deep.Dive.Into.Modeling.With.Blender.3D.rar.html[/code] [b]Links are Interchangeable - No Password - Single Extraction[/b]
  11. Skillshare - Blender 3D: Deep Dive Into Modeling With Blender 3D Genre: eLearning | Language: English | 3D Tutorials | MP4 This is the perfect place to master modeling tools in Blender. These skills are necessary for anyone looking to pursue a 3D career. This course will cover skills traditionally used in animation, motion design, and video game design. We will be going through the modeling techniques to model characters like this while learning the modeling workflow in Blender. You'll learn the tools, what it means to have good topology, and tricks to speed up your workflow. Feel free to follow along with the class example or make your own! Content Source: https://www.skillshare.com/classes/Blender-3D-Deep-Dive-Into-Modeling-With-Blender-3D/1537563083 https://uploadgig.com/file/download/3F25A8336e9691aE/Blender-3D-Deep-Dive-Into-Modeling-With-Blender-3D.rar https://rapidgator.net/file/78d2165297144dc4c326a2ae8ee1de80/Blender-3D-Deep-Dive-Into-Modeling-With-Blender-3D.rar.html https://hot4share.com/g6mfzjh4ml2e/Blender-3D-Deep-Dive-Into-Modeling-With-Blender-3D.rar.html https://ddownload.com/x3q3gstv6mfl/Blender-3D-Deep-Dive-Into-Modeling-With-Blender-3D.rar
  12. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 13 lectures (2h 33m) | Size: 987.6 MB Graph Neural Networks What you'll learn: Deep Learning for Natural Language Processing Graph Neural Networks Graph convolutions Graph pooling Applications of GNNs for NLP DL for NLP Requirements Basics of machine learning Basic understanding of convolution and pooling operations Description More and more evidence has demonstrated that graph representation learning especially graph neural networks (GNNs) has tremendously facilitated computational tasks on graphs including both node-focused and graph-focused tasks. The revolutionary advances brought by GNNs have also immensely contributed to the depth and breadth of the adoption of graph representation learning in real-world applications. For the classical application domains of graph representation learning such as recommender systems and social network analysis, GNNs result in state-of-the-art performance and bring them into new frontiers. Meanwhile, new application domains of GNNs have been continuously emerging such as combinational optimization, physics, and healthcare. These wide applications of GNNs enable diverse contributions and perspectives from disparate disciplines and make this research field truly interdisciplinary. In this course, I will start by talking about basic graph data representation and concepts like node data, edge types, adjacency matrix and Laplacian matrix etc. Next, we will talk about broad kinds of graph learning tasks and discuss basic operations needed in a GNN: filtering and pooling. Further, we will discuss details of different types of graph filtering (i.e., neighborhood aggregation) methods. These include graph convolutional networks, graph attention networks, confidence GCNs, Syntactic GCNs and the general message passing neural network framework. Next, we will talk about three main types of graph pooling methods: Topology based pooling, Global pooling and Hierarchical pooling. Within each of these three types of graph pooling methods, we will discuss popular methods. For example, in topology pooling we will talk about Normalized Cut and Graclus mainly. In Global pooling, we will talk about Set2Set and SortPool. In Hierarchical pooling, we will talk about diffPool, gPool and SAGPool. Next, we will talk about three unsupervised graph neural network architectures: GraphSAGE, Graph auto-encoders and Deep Graph InfoMax. Lastly, we will talk about some applications of GNNs for NLP including semantic role labeling, event detection, multiple event extraction, neural machine translation, document timestamping and relation extraction. Who this course is for Beginners in deep learning Python developers interested in data science concepts Masters or PhD students who wish to learn deep learning concepts quickly Deep learning engineers and developers Homepage https://www.udemy.com/course/ahol-dl4nlp8/ https://hot4share.com/sqcgn4449rw1/zqf38.Deep.Learning.for.NLP..Part.8.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/B967ebBa54aceA0c/zqf38.Deep.Learning.for.NLP..Part.8.rar Download ( Rapidgator ) https://rapidgator.net/file/9a25c9c84b79b2551afa0bc0851dcf3b/zqf38.Deep.Learning.for.NLP..Part.8.rar.html Download ( NitroFlare ) http://nitro.download/view/89F2C61AC15C200/zqf38.Deep.Learning.for.NLP..Part.8.rar Links are Interchangeable - No Password - Single Extraction
  13. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 36 lectures (6h 4m) | Size: 2.67 GB Model Compression for NLP What you'll learn: Deep Learning for Natural Language Processing Model Compression for NLP Pruning Quantization Knowledge Distillation Parameter sharing Matrix decomposition DL for NLP Requirements Basics of machine learning Basic understanding of Transformer based models and word embeddings Transformer Models like BERT and GPT Description In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs) networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-training Transformer (GPT-2), Multi-task Deep Neural Network (MT-DNN), Extra-Long Network (XLNet), Text-to-text transfer transformer (T5), T-NLG and GShard. These models are humongous in size: BERT (340M parameters), GPT-2 (1.5B parameters), T5 (11B parameters, 21.7GB), etc. On the other hand, real world applications demand small model size, low response times and low computational power wattage. In this course, we discuss five different types of methods (Pruning, Quantization, Knowledge Distillation, Parameter Sharing, Tensor Decomposition) for compression of such models to enable their deployment in real industry NLP projects. Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this course organizes the plethora of work done by the "deep learning for NLP" community in the past few years and presents it as a coherent story. Compression for deep learning text models has gained a lot of interest in recent years both from the research community and the industry. Many business owners shy away from using deep learning models fearing the model sizes and infrastructure requirements. Mobile apps need to have a low RAM footprint and clearly a small power envelope. IoT (Internet of Things) and embedded systems related organizations have been investing significantly in designing machine learning solutions for resource constrained environments like sensors. Researchers in the field of applied deep learning for text will benefit the most, as this tutorial will give them an exhaustive overview of the research in the direction of practical deep learning. We believe that the tutorial will give the newcomers a complete picture of the current work, introduce important research topics in this field, and inspire them to learn more. Practitioners and people from the industry will clearly benefit from the discussions both from the methods perspective, as well from the point of view of applications where such mechanisms are starting to be deployed. This tutorial can be considered an intermediate level tutorial where we assume the folks in audience to know some basic deep learning architectures. Prerequisite knowledge includes introductory level knowledge in deep learning, specifically recurrent neural networks models, and transformers. Also, basic understanding of natural language processing and machine learning concepts is expected. Who this course is for Beginners in deep learning Python developers interested in data science concepts Masters or PhD students who wish to learn deep learning concepts quickly Deep learning engineers and developers Homepage https://www.udemy.com/course/ahol-dl4nlp7/ https://hot4share.com/6em83e5vivti/47eiz.Deep.Learning.for.NLP..Part.7.part1.rar.html https://hot4share.com/5lhtatvp4jai/47eiz.Deep.Learning.for.NLP..Part.7.part2.rar.html https://hot4share.com/lix5mp7cz1ii/47eiz.Deep.Learning.for.NLP..Part.7.part3.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/EaAc2b2eCd31c34B/47eiz.Deep.Learning.for.NLP..Part.7.part1.rar https://uploadgig.com/file/download/804E2Eda376bc30e/47eiz.Deep.Learning.for.NLP..Part.7.part2.rar https://uploadgig.com/file/download/28429d05287c9faC/47eiz.Deep.Learning.for.NLP..Part.7.part3.rar Download ( Rapidgator ) https://rapidgator.net/file/1ca9c19cd36aabaf09b833b0ccda5821/47eiz.Deep.Learning.for.NLP..Part.7.part1.rar.html https://rapidgator.net/file/3d35358ca27665b45ad634bfb10a3778/47eiz.Deep.Learning.for.NLP..Part.7.part2.rar.html https://rapidgator.net/file/8b91190897d65add6a66a0dd2e9d5a74/47eiz.Deep.Learning.for.NLP..Part.7.part3.rar.html Download ( NitroFlare ) http://nitro.download/view/E245685C648E240/47eiz.Deep.Learning.for.NLP..Part.7.part1.rar http://nitro.download/view/ECDDCB32DDC89C8/47eiz.Deep.Learning.for.NLP..Part.7.part2.rar http://nitro.download/view/93EE8BA7FC57282/47eiz.Deep.Learning.for.NLP..Part.7.part3.rar Links are Interchangeable - No Password - Single Extraction
  14. Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 31.6 GB | Duration: 31h 22m Become a credible Data Scientist & Machine Learning expert | learn to code: Python, Keras, Pandas, Colab What you'll learn Artificial Intelligence, Machine Learning and Deep Learning, Data Science, Data Scientist Coding, Code python, keras, colab, pandas Machine Learning Fundamentals and Math refresher for Machine Learning: linear algebra, calculus, statistics Computer Vision, NLP, Naive Bayes, XGBoost, Logistic Regression, Bagging, Boosting, Radom Forest, Transformers, LSTM, GRU, Anomaly Detection, Clustering Dropout, Backpropagation, Gradient Descent, Variational auto-encoders, Covnets, Recurrent Neural Nets, Recommender Systems, LOF, Support Vector Machines (SVM) Data Augmentation, KNN, Collaborative Filtering, GloVe, Word2Vec, Resnet, VGG19, Adam, RMSprop, Adaboost, Momentum, hyperparameter Description Imagine being frustrated because you do not understand what AI, Machine Learning and Deep Learning are all about. Mastering the subjects of this course will give you access to a variety high paying jobs. This is the only course on the market that explains every little detail in a logical sequence. This course is absolutely no walk in the park and will require discipline. No prior knowledge is required and the course offers math refreshers in linear algebra, calculus and statistics. The course assumes that you have no prior coding experience. All the software that we use is open source and free. We will explain every block of code. The course is delivered through simple whiteboard and screen recording sessions. The code is made available via .ipynb files attached to the lecture itself. Notes are available for the majority of the lectures, except for lectures 1 to 12 as these lectures are more descriptive. Reference is made to my Github account (mfavaits) where some of the notes can be found as well. The majority of the notes are handwritten. 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  15. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 6.08 GB | Duration: 14h 34m What you'll learn Complete Understanding of Deep Learning from the Scratch Building the Artificial Neural Networks (ANNs) from the Scratch Artificial Neural Networks (ANNs) for Binary Data Classification Building Convolutional Neural Networks from the Scratch Convolutional Neural Network for Image Classification Convolutional Neural Network for Digit Recognition Breast Cancer Detection with Convolutional Neural Networks Convolutional Neural Networks for Predictive Analysis Convolutional Neural Networks for Fraud Detection Building the Recurrent Neural Networks (ANNs) from Scratch LSTM and GRU Review Classification with LSTM and GRU LSTM and GRU for Image Classification Prediction of Google Stock Price with RNN and LSTM Transfer Learning Natural Language Processing Crash Course on Numpy (Data Analysis) Crash Course on Pandas (Data Analysis) Crash course on MatDescriptionlib (Data Visualization) Requirements Python Programming Basics Description The Artificial Intelligence and Deep Learning are growing exponentially in today's world. There are multiple application of AI and Deep Learning like Self Driving Cars, Chat-bots, Image Recognition, Virtual Assistance, ALEXA, so on... With this course you will understand the complexities of Deep Learning in easy way, as well as you will have A Complete Understanding of Googles TensorFlow 2.0 Framework TensorFlow 2.0 Framework has amazing features that simplify the Model Development, Maintenance, Processes and Performance In TensorFlow 2.0 you can start the coding with Zero Installation, whether you're an expert or a beginner, in this course you will learn an end-to-end implementation of Deep Learning Algorithms List of the Projects that you will work on, Part 1: Artificial Neural Networks (ANNs) Project 1: Multiclass image classification with ANN Project 2: Binary Data Classification with ANN Part 2: Convolutional Neural Networks (CNNs) Project 3: Object Recognition in Images with CNN Project 4: Binary Image Classification with CNN Project 5: Digit Recognition with CNN Project 6: Breast Cancer Detection with CNN Project 7: Predicting the Bank Customer Satisfaction Project 8: Credit Card Fraud Detection with CNN Part 3: Recurrent Neural Networks (RNNs) Project 9: IMDB Review Classification with RNN - LSTM Project 10: Multiclass Image Classification with RNN - LSTM Project 11: Google Stock Price Prediction with RNN and LSTM Part 4: Transfer Learning Part 5: Natural Language Processing Basics of Natural Language Processing Project 12: Movie Review Classifivation with NLTK Part 6: Data Analysis and Data Visualization Crash Course on Numpy (Data Analysis) Crash Course on Pandas (Data Analysis) Crash course on MatDescriptionlib (Data Visualization) With this course you will learn, 1) To built the Neural Networks from the scratch 2) You will have a complete understanding of Artificial Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks 3) You will learn to built the neural networks with LSTM and GRU 4) Hands On Transfer Learning 5) Learn Natural Language Processing by doing a text classifiation project 6) Improve your skills in Data Analysis with Numpy, Pandas and Data Visualization with MatDescriptionlib So what are you waiting for, Enroll Now and understand Deep Learning to advance your career and increase your knowledge ! Regards, Vijay Gadhave Who this course is for: Anyone who wants to learn Deep Learning and AI Students and Professionals who want to start a career in Data Science, Deep Learning and AI Homepage https://www.udemy.com/course/deep-learning-for-beginners-with-tensorflow-20-and-python/ https://hot4share.com/xnsoob7tflvn/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part1.rar.html https://hot4share.com/m8f4p1lvrejj/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part2.rar.html https://hot4share.com/tbo9bafabqju/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part3.rar.html https://hot4share.com/ye93lnhe9lr7/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part4.rar.html https://hot4share.com/lmrung33ioun/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part5.rar.html https://hot4share.com/jtulrekb1umn/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part6.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/020e92a7365466D7/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part1.rar https://uploadgig.com/file/download/B32a16Eb3855b753/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part2.rar https://uploadgig.com/file/download/5828d2Cfcce5306F/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part3.rar https://uploadgig.com/file/download/aC64e82Cd8145050/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part4.rar https://uploadgig.com/file/download/4dfd4aE8e7d5e042/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part5.rar https://uploadgig.com/file/download/bfeEB7eac3329d42/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part6.rar Download ( Rapidgator ) https://rapidgator.net/file/15b256b55cb5d0cec1a895fe221f9448/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part1.rar.html https://rapidgator.net/file/71ba18d8c03eaccfd044de4b1368baae/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part2.rar.html https://rapidgator.net/file/9bfb2db2faad94f3d98aff4d1b908862/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part3.rar.html https://rapidgator.net/file/3cbabd29fadfa1b2a5f70566b531dad4/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part4.rar.html https://rapidgator.net/file/6125d080be1ac4cd86a0d8f0d32dcc4d/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part5.rar.html https://rapidgator.net/file/589e700b8768a5f94c798dd108ac9242/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part6.rar.html Download ( NitroFlare ) http://nitro.download/view/F75A23D7E52DD87/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part1.rar http://nitro.download/view/EFCABD3BAA6FF48/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part2.rar http://nitro.download/view/50B18E8F78A66AF/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part3.rar http://nitro.download/view/DABD5443E9C126A/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part4.rar http://nitro.download/view/EC4F2C353C41628/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part5.rar http://nitro.download/view/80FFA05F84BA254/51ua4.Deep.Learning.for.Beginners.in.Python.Work.On.12.Projects.part6.rar Links are Interchangeable - No Password - Single Extraction
  16. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 10.2 GB | Duration: 18h 0m What you'll learn Time Series Decomposition Univariate analysis for time series Bivariate analysis and auto-correlation Smoothing the time series seasonally adjusting the time series Generating and Calibrating Forecasting in Excel Learning R and using it as everyday tool for forecasting Using the Fable Package for advanced forecasting methods and aggregations Time Series Forecasting Different Applications of forecasting R Fable Business Forecasting Excel Requirements Nop Description Hello :) Forecasting has been around for 1000s of years. it stems from our need to plan so we can have some direction for the future. We can consider forecasting as the stepping stone for planning. and that's why it is as important as ever to have good forecasters in institutions, supply chains, companies, and businesses. With the ever-growing concerns of sustainability and Carbon-footprint. Would you believe it? a good forecast actually contributes to saving resources through the value chain and actually saving the planet. one forecaster at a time. needless to mention, forecasting is integral in marketing, operations, finance, and planning for supply chains.... pretty much everything This course is aimed to orient you to the latest statistical forecasting techniques and trends. but first, we need to understand how forecasting works and the reasoning behind statistical methods, and when each method is suitable to be used. that's why we start first with excel and we scale with R. "Don't worry if you don't know R, Crash fundamental sections are included!. the course is for all levels because we start from Zero to Hero in Forecasting. in this course we will learn and apply : 1- Time Series Decomposition in Excel and R. 2- Univariate analysis for time series in Excel and R. 3- Bivariate analysis and auto-correlation in Excel and R. 4- Smoothing the time series and getting the Trend with Double and centered moving average. 5- seasonally adjusting the time series. 6- Simple and complex forecasts in Excel. 7- Use transformations to reduce the variance while forecasting. 8-Generating and Calibrating Forecasting in Excel. 9- Learning R and using it as an everyday tool for forecasting. 10- Using the Fable Package for advanced forecasting methods and aggregations. 11- Using Forecast package for grid search on ARIMA. 12- Applying a workflow of different models in two lines of code. 13- Calibirating forecasting methods. 14- Applying Hierarchical time series with Bottom-up, middle out, and Top-down Approaches. 16- Use the new R-Fable reconciliation method for aggregation. 15- Using Fable to generate forecasts for 10000 time-series and much more !! *NOTE: Many of the concepts and analysis I explain first in excel as I find excel the best way to first explain a concept and then we scale up, improve and generalize with R. By the end of this course, you will have an exciting set of skills and a toolbox you can always rely on when tackling forecasting challenges. Happy Forecasting! Haytham Rescale Analytics Feedback from Clients and Training: "In Q4 2018, I was fortunate to find an opportunity to learn R in Dubai, after hearing about it from indirect references in UK. I attended a Supply Chain Forecasting & Demand Planning Masterclass conducted by Haitham Omar and the possibilities seemed endless. So, we requested Haitham to conduct a 5-day workshop in our office to train 8 staff members, which opened us up as a team to deeper data analysis. Today, we have gone a step further and retained Haitham, as a consultant, to take our data analysis to the next level and to help us implement inventory guidelines for our business. The above progression of our actions is a clear indication of the capabilities of Haitham as a specialist in R and in data analytics, demand planning, and inventory management." Shailesh Mendonca Commercial lead-in Adventure AHQ- Sharaf Group " Haytham mentored me in my Role of Head of Supply Chain efficiency. He is extremely knowledgebase about the supply concepts, latest trends, and benchmarks in the supply chain world. Haytham's analytics-driven approach was very helpful for me to recommend and implement significant changes to our supply chain at Aster group" Saify Naqvi Head of Supply Chain Efficiency "I participated to the training session called "Supply Chain Forecasting & Management" on December 22nd 2018. This training helped me a lot in my daily work since I am working in Purchase Dpt. Haytham has the pedagogy to explain us very difficult calculations and formula in a simple way. I highly recommend this training." Djamel BOUREMIZ Purchasing Manager at Mineral Circles Bearings Who this course is for: Planners Strategists Retail merchandise Financiers Supply chain Economists Operation managers Budgeters Homepage https://www.udemy.com/course/a-deep-dive-into-statistical-forecasting/ https://hot4share.com/926n0x5fpbru/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part01.rar.html https://hot4share.com/sy74crrlu6q7/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part02.rar.html https://hot4share.com/1rtronefsxqg/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part03.rar.html https://hot4share.com/i25kxhanjiwe/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part04.rar.html https://hot4share.com/t3udrqsf6ct6/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part05.rar.html https://hot4share.com/v00zhqequ4fh/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part06.rar.html https://hot4share.com/tz83xooyt9nm/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part07.rar.html https://hot4share.com/bhrjye586ikz/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part08.rar.html https://hot4share.com/srxv7jhk81yq/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part09.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/22d4d32cD6d5248F/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part01.rar https://uploadgig.com/file/download/D167CEd80005a0ef/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part02.rar https://uploadgig.com/file/download/10Fec904221f4e66/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part03.rar https://uploadgig.com/file/download/ad86A4bc124Dd408/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part04.rar https://uploadgig.com/file/download/7555dc37377264df/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part05.rar https://uploadgig.com/file/download/0C9babB958b4024e/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part06.rar https://uploadgig.com/file/download/fc95986b37f1f10c/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part07.rar https://uploadgig.com/file/download/1C7442e94982269e/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part08.rar https://uploadgig.com/file/download/42E91790040FC56a/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part09.rar Download ( Rapidgator ) https://rapidgator.net/file/f97e3964ccc67bcbd659df2cc9de1a0f/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part01.rar.html https://rapidgator.net/file/b76acae454b01752b302f666920cc366/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part02.rar.html https://rapidgator.net/file/9aff2b183b15765a9cbf7a5440f68603/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part03.rar.html https://rapidgator.net/file/a52c8b573cbac7695a455bd14d1ef1ea/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part04.rar.html https://rapidgator.net/file/4e976fc0635225acd4591af54557c648/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part05.rar.html https://rapidgator.net/file/8f125f7ae26dde9bd766b8dc803fea13/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part06.rar.html https://rapidgator.net/file/23f9fc6051e4bd24ae2809638130016c/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part07.rar.html https://rapidgator.net/file/d0db32ee12fc825145a734d8fcce1db4/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part08.rar.html https://rapidgator.net/file/a4ed2c3dea86635ad8553fa63541a2c3/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part09.rar.html Download ( NitroFlare ) http://nitro.download/view/D519226610BA4C5/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part01.rar http://nitro.download/view/E0324A9495A9B89/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part02.rar http://nitro.download/view/AA3A633A3307B71/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part03.rar http://nitro.download/view/111AEB563992621/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part04.rar http://nitro.download/view/E602EBC610FDB29/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part05.rar http://nitro.download/view/C906BD12816F6F6/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part06.rar http://nitro.download/view/C2A33BCD9D5CCA9/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part07.rar http://nitro.download/view/7D31515D68FD562/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part08.rar http://nitro.download/view/E42977F68FB4A8F/2mu1e.A.Deep.Dive.into.Statistical.ForecastingExcel..R.part09.rar Links are Interchangeable - No Password - Single Extraction
  17. Deep Down 2021 1080p WEB h264-KOGi File Size: 795.65 MB Year(2021):LanguageEnglish: Title: Deep down Genres: Short, Drama, Romance [color=#ff3333][b]iMDB info[/b][/color]: [quote]https://www.imdb.com/title/tt10230284/[/quote] Description: Mia needs someone to go with her to a fertility clinic because she will have anesthesia. She asks her ex-boyfriend to go. The story explores the interactions of the characters both before and after. Video: Width: 1920 pixels Height: 1080 pixels Duration: 00:12:54 Preview images Download Links Download from RapidGator https://rapidgator.net/file/6c8f41a52adc60a9f8f2a2f5072adf82/deep.down.2021.1080p.web.h264-kogi.part1.rar https://rapidgator.net/file/7bdc9b470c91c3b46d7f6b18a2e5c306/deep.down.2021.1080p.web.h264-kogi.part2.rar Download from NitroFlare https://nitro.download/view/E0C9FCD38C95CB7/deep.down.2021.1080p.web.h264-kogi.part1.rar https://nitro.download/view/4CF46B1BBF82D38/deep.down.2021.1080p.web.h264-kogi.part2.rar
  18. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 74 lectures (8h 21m) | Size: 5.24 GB Computer Vision with CNN: Basic Python, Numpy, Pandas, MatDescriptionlib, Keras Text MLP, VGGNet, ResNet, Custom Model in Colab What you'll learn: Deep Learning Computer Vision Keras Machine Learning Python Requirements Basic computer knowledge and an interest to learn the Deep Learning using Keras Description Welcome to my new course 'Deep Learning from the Scratch using Python and Keras'. This is the second course of my Deep Learning Series. As you already know the artificial intelligence domain is divided broadly into deep learning and machine learning. In-fact deep learning is machine learning itself but Deep learning with its deep neural networks and algorithms try to learn high-level features from data without human intervention. That makes deep learning the base of all future self intelligent systems. And in this course, I am starting from the very basic things to learn like learning the programming language basics and other supporting libraries at first and proceed with the core topic. Let's see what are the interesting topics included in this course. At first we will have an introductory theory session about Artificial Intelligence, Machine learning, Artificial Neurons based Deep Learning and Neural Networks. After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package and will check and see if everything is installed fine. We will be using the browser based IDE called Jupyter notebook for our further coding exercises. I know some of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions List and Tuples, Dictionaries, Functions etc. Then we will start with learning the basics of the Python Numpy library which is used to adding support for large, multi-dimensional arrays and matrices, along with a large collection of classes and functions. Then we will learn the basics of matDescriptionlib library which is a Descriptionting library for Python for corresponding numerical expressions in NumPy. And finally the pandas library which is a software library written for the Python programming language for data manipulation and analysis. After the basics, we will then install the deep learning libraries theano, tensorflow and the API for dealing with these called as Keras. We will be writing all our future codes in keras. Then before we jump into deep learning, we will have an elaborate theory session about the basic Basic Structure of an Artificial Neuron and how they are combined to form an artificial Neural Network. Then we will see what exactly is an activation function, different types of most popular activation functions and the different scenarios we have to use each of them. After that we will see about the loss function, the different types of popular loss functions and the different scenarios we have to use each of them. Like the Activation and loss functions, we have optimizers which will optimize the neural network based on the training feedback. We will also see the details about most popular optimizers and how to decide in which scenarios we have to use each of them. Then finally we will discuss about the most popular deep learning neural network types and their basic structure and use cases. Further the course is divided into exactly two halves. The first half is about creating deep learning multi-layer neural network models for text based dataset and the second half about creating convolutional neural networks for image based dataset. In Text based simple feed forward multi-layer neural network model we will start with a regression model to predict house prices of King County USA. The first step will be to Fetch and Load Dataset from the kaggle website into our program. Then as the second step, we will do an EDA or an Exploratory Data Analysis of the loaded data and we will then prepare the data for giving it into our deep learning model. Then we will define the Keras Deep Learning Model. Once we define the model, we will then compile the model and later we will fit our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics like accuracy, loss etc can be evaluated and visualized using matDescriptionlib. Finally we have our already trained model. We will try doing a prediction of the king county real estate price using our deep learning model and evaluate the results. That was a text based regression model. Now we will proceed with a text based binary classification model. We will be using a derived version of Heart Disease Data Set from the UCI Machine Learning Repository. Our aim is to predict if a person will be having heart disease or not from the learning achieved from this dataset. The same steps repeat here also. The first step will be to Fetch and Load Dataset into our program. Then as the second step, we will do an EDA or an Exploratory Data Analysis of the loaded data and we will then prepare the data for giving it into our deep learning model. Then we will define the Keras Deep Learning Model. Once we define the model, we will then compile the model and later we will fit our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics like accuracy, loss etc can be evaluated and visualized using matDescriptionlib. Finally we have our already trained model. We will try doing a prediction for heart disease using our deep learning model and evaluate the results. After the text based binary classification model. Now we will proceed with a text based multi class classification model. We will be using the Red Wine Quality Data Set from the kaggle website. Our aim is to predict the multiple categories in which a redwine sample can be placed from the learning achieved from this dataset. The same steps repeat here also. The first step will be to Fetch and Load Dataset into our program. Then as the second step, we will do an EDA or an Exploratory Data Analysis of the loaded data and we will then prepare the data for giving it into our deep learning model. Then we will define the Keras Deep Learning Model. Once we define the model, we will then compile the model and later we will fit our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics like accuracy, loss etc can be evaluated and visualized using matDescriptionlib. Finally we have our already trained model. We will try doing a prediction for wine quality with a new set of data and then evaluate the categorical results. We may be spending much time, resources and efforts to train a deep learning model. We will learn about the techniques to save an already trained model. This process is called serialization. We will at first serialize a model. Then later load it in another program and do the prediction without having to repeat the training. That was about text based data. We will now proceed with image based data. In the preliminary session we will have an introduction to Digital Image Basics in which we learn about the composition and structure of a digital image. Then we will learn about Basic Image Processing using Keras Functions. There are many classes and functions that help with pre processing an image in the Keras library api. We will learn about the most popular and useful functions one by one. Another important and useful image processing function in keras is Image Augmentation in which slightly different versions of images are automatically created during training. We will learn about single image augmentation, augmentation of images within a directory structure and also data frame image augmentation. Then another theory session about the basics of a Convolutional neural network or CNN. We will learn how the basic CNN layers like convolution layer, the pooling layer and the fully connected layer works. There are concepts like Stride Padding and Flattening in convolution for image processing. We will learn them also one by one. Now we are all set to start with our CNN Model. We will be designing a model that can classify 5 different types of flowers if provided with an image of a flower in any of these categories. We will be at first downloading the dataset from the kaggle website. Then the first step will be to Fetch and Load this Dataset from our computer into our program. Then as the second step, we have to split this dataset manually for training and then later testing the model. We will arrange them into training and testing folders with each class labelled in separate folders. Then we will define the Keras Deep Learning Model. Once we define the model, we will then compile the model and later we will fit our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics like accuracy, loss etc can be evaluated and visualized using matDescriptionlib. Finally we have our already trained model. We will try doing a prediction for five different types of flowers with a new set of image data and then evaluate the categorical results. There are many techniques which we can use to improve the quality of a model. Especially an image based model. The most popular techniques are doing dropout regularization of the model. The next technique is doing the optimization and adjustment of the padding and also the filters in the convolution layers. And finally optimization using image augmentation. We will tweak different augmentation options in this session. Doing these optimization techniques manually one by one and comparing results is a very tedious task. So we will be using a technique called Hyper parameter tuning in which the keras library itself will switch different optimization techniques that we specify and will report and compare the results without we having to interfere in it. Even though these techniques and creation of a model from the scratch is fun. Its very time consuming and may take ages if you are planning to design a large model. In this situation a technique called transfer learning can help us. We will take the world renounced, state of the art, most popular pre-trained deep learning models designed by experts and we will transfer the learning into our model so that we can make use of the architecture of that model into our custom model that we are building. The popular state of the art model architectures that we are going to use are the VGG16, VGG19 designed by deep learning experts from the University of Oxford and also ResNet50 created in ImageNet challenge to address the vanishing gradient problem. We will at first download these models using keras and will try simple predictions using these pre-trained models. Later we will try the network training for our flower dataset itself using the VGG16. we will make few changes in the model to incorporate our dataset into it. Since the network architecture is not that simple, in our computer it will take a lot of time to complete the training. So instead of CPU, we have to use a GPU to enhance parallel processing. We will be using a cloud based Free GPU service provied by goggle called Google Colab. At first we will try training with VGG16 in google colab. We will prepare, zip and upload the dataset into google colab. Then we will extract it using linux comands and then do the training. The training is almost ten times faster compared to the local computer. Once we have the trained model we will serialize the model and will do the prediction. The same procedure will be repeated for VGG19 and also for ResNet. And that's all about the topics which are currently included in this quick course. The code, images, models and weights used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked. Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio. So that's all for now, see you soon in the class room. Happy learning and have a great time. Who this course is for Beginner who wants to learn the Basic to Advanced Deep Learning Homepage https://www.udemy.com/course/deep-learning-computer-vision-using-keras-dummies-guide/ [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/fE3992c945f5887f/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part1.rar https://uploadgig.com/file/download/0ec5844C73b646CB/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part2.rar https://uploadgig.com/file/download/beA00F730e661b2f/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part3.rar https://uploadgig.com/file/download/Cfa8d85085Ccf689/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part4.rar https://uploadgig.com/file/download/ce693bde916c324f/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part5.rar https://uploadgig.com/file/download/e4a87b0d00592bE8/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part6.rar Download ( Rapidgator ) https://rapidgator.net/file/6c1ce6342f3a5bee287799b382e3d758/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part1.rar.html https://rapidgator.net/file/70901da2156841edca783be223155b33/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part2.rar.html https://rapidgator.net/file/7e22da7a66d773bbfe6c26c7b483cbf4/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part3.rar.html https://rapidgator.net/file/520302928dd46fe8b01f9d2de36c064a/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part4.rar.html https://rapidgator.net/file/f75a6bade8828fb3802fc45dac632b3b/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part5.rar.html https://rapidgator.net/file/a39b905237fe03f398a81435133d1915/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part6.rar.html Download ( NitroFlare ) http://nitro.download/view/4948452109FAD72/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part1.rar http://nitro.download/view/4893F65613BA1A1/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part2.rar http://nitro.download/view/9837765A3931030/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part3.rar http://nitro.download/view/06E5F4A980274C7/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part4.rar http://nitro.download/view/524D0A713AFE91C/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part5.rar http://nitro.download/view/EA1D358C87E8663/mpp0v.Deep.Learning.using.Keras..Complete..Compact.Dummies.Guide.part6.rar Links are Interchangeable - No Password - Single Extraction
  19. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 74 lectures (8h 21m) | Size: 5.24 GB Computer Vision with CNN: Basic Python, Numpy, Pandas, MatDescriptionlib, Keras Text MLP, VGGNet, ResNet, Custom Model in Colab What you'll learn: Deep Learning Computer Vision Keras Machine Learning Python Requirements Basic computer knowledge and an interest to learn the Deep Learning using Keras Description Welcome to my new course 'Deep Learning from the Scratch using Python and Keras'. This is the second course of my Deep Learning Series. As you already know the artificial intelligence domain is divided broadly into deep learning and machine learning. In-fact deep learning is machine learning itself but Deep learning with its deep neural networks and algorithms try to learn high-level features from data without human intervention. That makes deep learning the base of all future self intelligent systems. And in this course, I am starting from the very basic things to learn like learning the programming language basics and other supporting libraries at first and proceed with the core topic. Let's see what are the interesting topics included in this course. At first we will have an introductory theory session about Artificial Intelligence, Machine learning, Artificial Neurons based Deep Learning and Neural Networks. After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package and will check and see if everything is installed fine. We will be using the browser based IDE called Jupyter notebook for our further coding exercises. I know some of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions List and Tuples, Dictionaries, Functions etc. Then we will start with learning the basics of the Python Numpy library which is used to adding support for large, multi-dimensional arrays and matrices, along with a large collection of classes and functions. Then we will learn the basics of matDescriptionlib library which is a Descriptionting library for Python for corresponding numerical expressions in NumPy. And finally the pandas library which is a software library written for the Python programming language for data manipulation and analysis. After the basics, we will then install the deep learning libraries theano, tensorflow and the API for dealing with these called as Keras. We will be writing all our future codes in keras. Then before we jump into deep learning, we will have an elaborate theory session about the basic Basic Structure of an Artificial Neuron and how they are combined to form an artificial Neural Network. Then we will see what exactly is an activation function, different types of most popular activation functions and the different scenarios we have to use each of them. After that we will see about the loss function, the different types of popular loss functions and the different scenarios we have to use each of them. Like the Activation and loss functions, we have optimizers which will optimize the neural network based on the training feedback. We will also see the details about most popular optimizers and how to decide in which scenarios we have to use each of them. Then finally we will discuss about the most popular deep learning neural network types and their basic structure and use cases. Further the course is divided into exactly two halves. The first half is about creating deep learning multi-layer neural network models for text based dataset and the second half about creating convolutional neural networks for image based dataset. In Text based simple feed forward multi-layer neural network model we will start with a regression model to predict house prices of King County USA. The first step will be to Fetch and Load Dataset from the kaggle website into our program. Then as the second step, we will do an EDA or an Exploratory Data Analysis of the loaded data and we will then prepare the data for giving it into our deep learning model. Then we will define the Keras Deep Learning Model. Once we define the model, we will then compile the model and later we will fit our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics like accuracy, loss etc can be evaluated and visualized using matDescriptionlib. Finally we have our already trained model. We will try doing a prediction of the king county real estate price using our deep learning model and evaluate the results. That was a text based regression model. Now we will proceed with a text based binary classification model. We will be using a derived version of Heart Disease Data Set from the UCI Machine Learning Repository. Our aim is to predict if a person will be having heart disease or not from the learning achieved from this dataset. The same steps repeat here also. The first step will be to Fetch and Load Dataset into our program. Then as the second step, we will do an EDA or an Exploratory Data Analysis of the loaded data and we will then prepare the data for giving it into our deep learning model. Then we will define the Keras Deep Learning Model. Once we define the model, we will then compile the model and later we will fit our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics like accuracy, loss etc can be evaluated and visualized using matDescriptionlib. Finally we have our already trained model. We will try doing a prediction for heart disease using our deep learning model and evaluate the results. After the text based binary classification model. Now we will proceed with a text based multi class classification model. We will be using the Red Wine Quality Data Set from the kaggle website. Our aim is to predict the multiple categories in which a redwine sample can be placed from the learning achieved from this dataset. The same steps repeat here also. The first step will be to Fetch and Load Dataset into our program. Then as the second step, we will do an EDA or an Exploratory Data Analysis of the loaded data and we will then prepare the data for giving it into our deep learning model. Then we will define the Keras Deep Learning Model. Once we define the model, we will then compile the model and later we will fit our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics like accuracy, loss etc can be evaluated and visualized using matDescriptionlib. Finally we have our already trained model. We will try doing a prediction for wine quality with a new set of data and then evaluate the categorical results. We may be spending much time, resources and efforts to train a deep learning model. We will learn about the techniques to save an already trained model. This process is called serialization. We will at first serialize a model. Then later load it in another program and do the prediction without having to repeat the training. That was about text based data. We will now proceed with image based data. In the preliminary session we will have an introduction to Digital Image Basics in which we learn about the composition and structure of a digital image. Then we will learn about Basic Image Processing using Keras Functions. There are many classes and functions that help with pre processing an image in the Keras library api. We will learn about the most popular and useful functions one by one. Another important and useful image processing function in keras is Image Augmentation in which slightly different versions of images are automatically created during training. We will learn about single image augmentation, augmentation of images within a directory structure and also data frame image augmentation. Then another theory session about the basics of a Convolutional neural network or CNN. We will learn how the basic CNN layers like convolution layer, the pooling layer and the fully connected layer works. There are concepts like Stride Padding and Flattening in convolution for image processing. We will learn them also one by one. Now we are all set to start with our CNN Model. We will be designing a model that can classify 5 different types of flowers if provided with an image of a flower in any of these categories. We will be at first downloading the dataset from the kaggle website. Then the first step will be to Fetch and Load this Dataset from our computer into our program. Then as the second step, we have to split this dataset manually for training and then later testing the model. We will arrange them into training and testing folders with each class labelled in separate folders. Then we will define the Keras Deep Learning Model. Once we define the model, we will then compile the model and later we will fit our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics like accuracy, loss etc can be evaluated and visualized using matDescriptionlib. Finally we have our already trained model. We will try doing a prediction for five different types of flowers with a new set of image data and then evaluate the categorical results. There are many techniques which we can use to improve the quality of a model. Especially an image based model. The most popular techniques are doing dropout regularization of the model. The next technique is doing the optimization and adjustment of the padding and also the filters in the convolution layers. And finally optimization using image augmentation. We will tweak different augmentation options in this session. Doing these optimization techniques manually one by one and comparing results is a very tedious task. So we will be using a technique called Hyper parameter tuning in which the keras library itself will switch different optimization techniques that we specify and will report and compare the results without we having to interfere in it. Even though these techniques and creation of a model from the scratch is fun. Its very time consuming and may take ages if you are planning to design a large model. In this situation a technique called transfer learning can help us. We will take the world renounced, state of the art, most popular pre-trained deep learning models designed by experts and we will transfer the learning into our model so that we can make use of the architecture of that model into our custom model that we are building. The popular state of the art model architectures that we are going to use are the VGG16, VGG19 designed by deep learning experts from the University of Oxford and also ResNet50 created in ImageNet challenge to address the vanishing gradient problem. We will at first download these models using keras and will try simple predictions using these pre-trained models. Later we will try the network training for our flower dataset itself using the VGG16. we will make few changes in the model to incorporate our dataset into it. Since the network architecture is not that simple, in our computer it will take a lot of time to complete the training. So instead of CPU, we have to use a GPU to enhance parallel processing. We will be using a cloud based Free GPU service provied by goggle called Google Colab. At first we will try training with VGG16 in google colab. We will prepare, zip and upload the dataset into google colab. Then we will extract it using linux comands and then do the training. The training is almost ten times faster compared to the local computer. Once we have the trained model we will serialize the model and will do the prediction. The same procedure will be repeated for VGG19 and also for ResNet. And that's all about the topics which are currently included in this quick course. The code, images, models and weights used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked. Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio. So that's all for now, see you soon in the class room. Happy learning and have a great time. 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  20. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 937 MB | Duration: 2h 28m What you'll learn Fundamental Machine Learning & Deep Learning Linear Regression, Logistic Regression, Perceptron and Neural Network Detailed explanation about the four ML & DL models Why Neural Networks are better? Requirements Basic Mathematics Description Why this Course? Lot of us might have experienced difficulty when relating Machine Learning and Deep Learning models. This course aims to answer usual doubts such as, Why Deep Learning? Why Neural Network performs better than Machine Learning models? Deep Learning and Machine Learning are totally different technologies or they are much related? How Deep Learning evolved from Machine Learning? What it Covers? The course covers Machine Learning models such as Linear Regression, Perceptron, Logistic Regression and a Deep Learning model Dense Neural Network. The four chapters (videos) of the course deal with the adult life of a Legend named Mr. S and show how he used the Machine Learning and Deep Learning models to solve interesting problems such as partying, dating, searching for soulmate and eventually marrying the suitable girl in his life. Through the journey of Mr. S, you will finally get to know why Neural Network performs better & how Machine Learning and Deep Learning are related. Videos contain interesting scenarios with simple numerical examples and explanations. Who can opt for this Course? This course will be highly useful for those individuals, Who does/doesn't have CS background and wants to understand Deep Learning technically without coding & too much mathematics. Who are getting started with Machine Learning or Deep Learning. Who seeks the answer: Why Neural Network perform better than Machine Learning models and how Deep Learning evolved from Machine Learning. Who does research AI and have fundamental doubts about functionality of Neural Networks. Who this course is for: Beginners who are curious about AI, Data Science, Machine Learning & Deep Learning Researchers who want to understand the fundamentals clearly Professionals who are eager to understand the capability & functionality of a Neural Network Non-CS professional who wants to exploit ML and DL Homepage https://www.udemy.com/course/from-machine-learning-to-deep-learning/ [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/D8CF3cfda23bc097/2ra84.From.Machine.Learning.to.Deep.Learning.2021.rar Download ( Rapidgator ) https://rapidgator.net/file/b91d63ec1c1dd42e246a1ac37f86d3a0/2ra84.From.Machine.Learning.to.Deep.Learning.2021.rar.html Download ( NitroFlare ) http://nitro.download/view/77D7D6FD81DFC18/2ra84.From.Machine.Learning.to.Deep.Learning.2021.rar Links are Interchangeable - No Password - Single Extraction
  21. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.38 GB | Duration: 2h 39m What you'll learn Deep Learning for Natural Language Processing Popular Transformer encoder and decoder models Multi-modal Transformer models Large scale Transformer models DL for NLP Requirements Basics of machine learning Basic understanding of Transformer based models and word embeddings Transformer Models like BERT and GPT Description This course is a part of "Deep Learning for NLP" Series. In this course, I will talk about various popular Transformer models beyond the ones I have already covered in the previous sessions in this series. Such Transformer models including encoder as well as decoder based models and differ in terms of various aspects like form of input, pretraining objectives, pretraining data, architecture variations, etc. These Transformer models have been all proposed after 2019 and some of them are also from early 2021. Thus, as of Aug 2021, these models are very recent and state of the art across multiple NLP tasks. The course consists of three main sections as follows. In the first section, I will talk about a few Transformer encoder and decoder models which extend the original Transformer framework. Specifically I will cover SpanBERT, Electra, DeBERTa and DialoGPT. SpanBERT, Electra and DeBERTa are Transformer encoders while DialoGPT is a Transformer decoder model. For each model, we will also talk about their architecture or pretraining differs from standard Transformer. We will also talk important results on various NLP tasks. In the second section, I will talk about multi-modal Transformer models. Multimodal learning has gained a lot of momentum in recent years. Thus, there was a need to come up with Transformer models which could handle text and image data together. In this part, I will cover VisualBERT and vilBERT which both process the multi-modal input very effectively. Both the models have many similarities. We will discuss about theri similarities and differences in detail. Lastly, in the third section, I will talk about lareg scale Transformer models. I will introduce the mixture of experts (MoE) architecture. Then I will talk about how GShard adapts the MoE architecture, and shows great results on massive multilingual machine translation. Lastly, I will discuss Switch Transformers which simplify the MoE routing algorithm and also do several engineering optimizations to reduce network communciation and computation costs and mitigate instabilities. In general, each of these papers is pretty long and thus it becomes very difficult and time consuming to understand them. In these sessions, I have tried to summarize them nicely bringing out the intuitions and tying the important concepts across such papers in a coherent story. Hope you will find it useful for your work and understanding. Who this course is for: Beginners in deep learning Python developers interested in data science concepts Masters or PhD students who wish to learn deep learning concepts quickly Homepage https://www.udemy.com/course/ahol-dl4nlp6/ [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/04aBc5Aad87678ac/hwq6e.Deep.Learning.for.NLP..Part.6.part1.rar https://uploadgig.com/file/download/9f0650590cF00A70/hwq6e.Deep.Learning.for.NLP..Part.6.part2.rar Download ( Rapidgator ) https://rapidgator.net/file/94b6d605bd365f895c6614b6faefc9fd/hwq6e.Deep.Learning.for.NLP..Part.6.part1.rar.html https://rapidgator.net/file/646232935099d57fbc0d36078ce000c7/hwq6e.Deep.Learning.for.NLP..Part.6.part2.rar.html Download ( NitroFlare ) http://nitro.download/view/E786553E37CE9C9/hwq6e.Deep.Learning.for.NLP..Part.6.part1.rar http://nitro.download/view/E784A59BC97D96F/hwq6e.Deep.Learning.for.NLP..Part.6.part2.rar Links are Interchangeable - No Password - Single Extraction
  22. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.56 GB | Duration: 3h 31m What you'll learn Deep Learning for Natural Language Processing Efficient Transformer Models: Star Transformers, Sparse Transformers, Reformer, Longformer, Linformer, Synthesizer Efficient Transformer Models: ETC (Extended Transformer Construction), Big bird, Linear attention Transformer, Performer, Sparse Sinkhorn Transformer, Routing transformers Efficient Transformer benchmark: Long Range Arena Comparison of various efficient Transformer methods DL for NLP Requirements Basics of machine learning Basic understanding of Transformer based models and word embeddings Description This course is a part of "Deep Learning for NLP" Series. In this course, I will talk about various design schemes for efficient Transformer models. These techniques will come in very handy for academic as well as industry participants. For industry use cases, Transformer models have been shown to lead to very high accuracy values across many NLP tasks. But they have quadratic memory as well as computational complexity making it very difficult to ship them. Thus, this course which focuses on methods to make Transformers efficient is very critical for anyone who wants to ship Transformer models as part of their products. Time and activation memory in Transformers grows quadratically with the sequence length. This is because in every layer, every attention head attempts to come up with a transformed representation for every position by "paying attention" to tokens at every other position. Quadratic complexity implies that practically the maximum input size is rather limited. Thus, we cannot extract semantic representation for long documents by passing them as input to Transformers. Hence, in this module we will talk about methods to address this challenge. The course consists of two main sections as follows. In the two sections, I will talk about Efficient Transformer Models, Efficient Transformer benchmark and a Comparison of various efficient Transformer methods. In the first section, I will talk about methods like Star Transformers, Sparse Transformers, Reformer, Longformer, Linformer, Synthesizer. In the second section, I will talk about methods like ETC (Extended Transformer Construction), Big bird, Linear attention Transformer, Performer, Sparse Sinkhorn Transformer, Routing transformers. Long Range Arena is a recent benchmark for evaluating models on long sequence tasks with respect to accuracy, memory usage and inference time. We will discuss details about long range arena and finally wrap up with a philosophical categorization of various efficient Transformer methods. For each method, we will discuss specific scheme for optimization, architecture and results obtained for pretraining as well as downstream tasks. Who this course is for: Beginners in deep learning Python developers interested in data science concepts Masters or PhD students who wish to learn deep learning concepts quickly Folks wanting to ship their products across regions and languages (internationalization of their learning/predictive/generative models) Homepage https://www.udemy.com/course/ahol-dl4nlp5/ [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/932abaff8CA00d06/o5rnj.Deep.Learning.for.NLP..Part.5.part1.rar https://uploadgig.com/file/download/b4e016394BbAb5Db/o5rnj.Deep.Learning.for.NLP..Part.5.part2.rar Download ( Rapidgator ) https://rapidgator.net/file/2fcb09eec93297611e68841326a9d72e/o5rnj.Deep.Learning.for.NLP..Part.5.part1.rar.html https://rapidgator.net/file/95cd5acdb202d986f207b9950b05d8f4/o5rnj.Deep.Learning.for.NLP..Part.5.part2.rar.html Download ( NitroFlare ) http://nitro.download/view/92D43B4F9B63A13/o5rnj.Deep.Learning.for.NLP..Part.5.part1.rar http://nitro.download/view/EC53D7336FCFBFD/o5rnj.Deep.Learning.for.NLP..Part.5.part2.rar Links are Interchangeable - No Password - Single Extraction
  23. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.23 GB | Duration: 2h 45m What you'll learn Deep Learning for Natural Language Processing Introduction to cross-lingual training Cross lingual benchmarks: XLNI, XGLUE, XTREME, XTREME-R Cross lingual models: mBERT, XLM, Unicoder, XLM-R, BERT with adaptors, XNLG, mBART, InfoXLM, FILTER, mT5 DL for NLP Requirements Basics of machine learning Basic understanding of Transformer based models and word embeddings Transformer Models like BERT and BART Description This course is a part of "Deep Learning for NLP" Series. In this course, I will introduce concepts like Cross lingual benchmarks and models. These concepts form the base for multi-lingual and cross-lingual processing using advanced deep learning models for natural language understanding and generation across languages. Often times, I hear from various product teams: "My product is in en-US only. I want to quickly scale to global markets with cost-effective solutions.", or "I have a new feature. How can I sim-ship to multiple markets?" This course is motivated by such needs. In this course the goal is to try to answer such questions. The course consists of two main sections as follows. In both the sections, I will talk about some cross-lingual models as well as benchmarks. In the first section, I will talk about cross-lingual benchmark datasets like XNLI and XGLUE. I will also talk about initial cross-lingual models like mBERT, XLM, Unicoder, XLM-R, and BERT with adaptors. Most of these models are encoder-based models. We will also talk about basic ways of cross-lingual modeling like translate-train, translate-test, multi-lingual translate-train-all, and zero shot cross-lingual transfer. In the second section, I will talk about cross-lingual benchmark datasets like XTREME and XTREME-R. I will also talk about cross-lingual models like XNLG, mBART, InfoXLM, FILTER and mT5. Some of these models are encoder-only models like InfoXLM or FILTER while others can be used for encoder-decoder cross-lingual modeling like XNLG, mBART and mT5. For each model, we will discuss specific pretraining losses, pretraining strategy, architecture and results obtained for pretraining as well as downstream tasks. Who this course is for: Beginners in deep learning Python developers interested in data science concepts Masters or PhD students who wish to learn deep learning concepts quickly Homepage https://www.udemy.com/course/ahol-dl4nlp4/ [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/72e80a2182f3677D/rzqq7.Deep.Learning.for.NLP..Part.4.part1.rar https://uploadgig.com/file/download/9aB57681d60434c9/rzqq7.Deep.Learning.for.NLP..Part.4.part2.rar Download ( Rapidgator ) https://rapidgator.net/file/b1f701873958560f588ec7da373dac78/rzqq7.Deep.Learning.for.NLP..Part.4.part1.rar.html https://rapidgator.net/file/98f0faae2a0942b9a8be33c23cc039de/rzqq7.Deep.Learning.for.NLP..Part.4.part2.rar.html Download ( NitroFlare ) http://nitro.download/view/B91BF0191E115FF/rzqq7.Deep.Learning.for.NLP..Part.4.part1.rar http://nitro.download/view/876C36326A0A5B9/rzqq7.Deep.Learning.for.NLP..Part.4.part2.rar Links are Interchangeable - No Password - Single Extraction
  24. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.58 GB | Duration: 3h 26m What you'll learn Deep Learning for Natural Language Processing Sentence Embeddings: Bag of words, Doc2Vec, SkipThought, InferSent, DSSM, USE, MTDNN, SentenceBERT Generative Transformer Models: UniLM, Transformer-XL and XLNet, MASS, BART, CTRL, T5, ProphetNet DL for NLP Requirements Basics of machine learning Recurrent Models: RNNs, LSTMs, GRUs and variants Basic understanding of Transformer based models and word embeddings Description This course is a part of "Deep Learning for NLP" Series. In this course, I will introduce concepts like Sentence embeddings and Generative Transformer Models. These concepts form the base for good understanding of advanced deep learning models for modern Natural Language Generation. The course consists of two main sections as follows. In the first section, I will talk about sentence embeddings. We will start with basic bag of words methods where sentence embedddings are obtained using an aggregation over word embeddings of constituent words. We will talk about averaged bag of words, word mover's distance, SIF and Power means method. Then we will discuss two unsupervised methods: Doc2Vec and SkipThought. Further, we will discuss about supervised sentence embedding methods like recursive neural networks, deep averaging networks and InferSent. CNNs can also be used for computing semantic similarity between two text strings; we will talk about DSSMs for the same. We will also discuss 3 multi-task learning methods including Universal Sentence Encodings and MT-DNN. Lastly, I will talk about SentenceBERT. In the second section, I will talk about multiple Generative Transformer Models. We will start with UniLM. Then we will talk about segment recurrence and relative position embeddings in Transformer-XL. Then get to XLNets which use Transformer-XL along with permutation language modeling. Next we will understand span masking in MASS and also discuss various noising methods on BART. We will then discuss about controlled natural language generation using CTRL. We will discuss how T5 models every learning task as a text-to-text task. Finally, we will discuss how ProphetNet extends 2-stream attention modeling from XLNet to n-stream attention modeling, thereby enabling n-gram predictions. Who this course is for: Beginners in deep learning Python developers interested in data science concepts Masters or PhD students who wish to learn deep learning concepts quickly Homepage https://www.udemy.com/course/ahol-dl4nlp3/ [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/C9983f3F077ee012/wmam5.Deep.Learning.for.NLP..Part.3.part1.rar https://uploadgig.com/file/download/106E9a3eA7044477/wmam5.Deep.Learning.for.NLP..Part.3.part2.rar Download ( Rapidgator ) https://rapidgator.net/file/80103379f5624c48e6dd7bf48953110a/wmam5.Deep.Learning.for.NLP..Part.3.part1.rar.html https://rapidgator.net/file/89265525ae1e82096b0c69ec1ae1d6fd/wmam5.Deep.Learning.for.NLP..Part.3.part2.rar.html Download ( NitroFlare ) http://nitro.download/view/1101DB73D713648/wmam5.Deep.Learning.for.NLP..Part.3.part1.rar http://nitro.download/view/1C08723AB93FDB2/wmam5.Deep.Learning.for.NLP..Part.3.part2.rar Links are Interchangeable - No Password - Single Extraction
  25. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.19 GB | Duration: 2h 52m What you'll learn Deep Learning for Natural Language Processing Encoder-decoder models, Attention models, ELMo GLUE, Transformers, GPT, BERT DL for NLP Requirements Basics of machine learning Recurrent Models: RNNs, LSTMs, GRUs and variants Multi-Layered Perceptrons (MLPs) Description This course is a part of "Deep Learning for NLP" Series. In this course, I will introduce concepts like Encoder-decoder attention models, ELMo, GLUE, Transformers, GPT and BERT. These concepts form the base for good understanding of advanced deep learning models for modern Natural Language Processing. The course consists of two main sections as follows. In the first section, I will talk about Encoder-decoder models in the context of machine translation and how beam search decoder works. Next, I will talk about the concept of encoder-decoder attention. Further, I will elaborate on different types of attention like Global attention, local attention, hierarchical attention, and attention for sentence pairs using CNNs as well as LSTMs. We will also talk about attention visualization. Finally, we will discuss ELMo which is a way of using recurrent models to compute context sensitive word embeddings. In the second section, I will talk about details about the various tasks which are a part of the GLUE benchmark and details about other benchmark NLP datasets across tasks. Then we will start our modern NLP journey with understanding different parts of an encoder-decoder Transformer model. We will delve into details of Transformers in terms of concepts like self attention, multi-head attention, positional embeddings, residual connections, and masked attention. After that I will talk about two most popular Transformer models: GPT and BERT. In the GPT part, we will discuss how is GPT trained and what are differences in variants like GPT2 and GPT3. In the BERT part, we will discuss how BERT is different from GPT, how it is pretrained using the masked language modeling and next sentence prediction tasks. We will also quickly talk about finetuning for BERT and multilingual BERT. Who this course is for: Beginners in deep learning Python developers interested in data science concepts Homepage https://www.udemy.com/course/ahol-dl4nlp2/ [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/4ec35D6849285C8b/9ku4r.Deep.Learning.for.NLP..Part.2.part1.rar https://uploadgig.com/file/download/ea0508e013AF4e13/9ku4r.Deep.Learning.for.NLP..Part.2.part2.rar Download ( Rapidgator ) https://rapidgator.net/file/a1ff85c652f11d9e2b6049abf9dde30e/9ku4r.Deep.Learning.for.NLP..Part.2.part1.rar.html https://rapidgator.net/file/b68d3518a63aa93b5f750eb252a47a8b/9ku4r.Deep.Learning.for.NLP..Part.2.part2.rar.html Download ( NitroFlare ) http://nitro.download/view/E2B2F2FE52615E3/9ku4r.Deep.Learning.for.NLP..Part.2.part1.rar http://nitro.download/view/D4D133F1DEDD2CA/9ku4r.Deep.Learning.for.NLP..Part.2.part2.rar Links are Interchangeable - No Password - Single Extraction
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