Applied AI through Deep Learning: Not only learn but create

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[img]https://a.radikal.ru/a02/2201/41/a1bfc2dc388f.jpg[/img]
[b]Applied AI through Deep Learning: Not only learn but create[/b]
Instructors: Junaid Zafar
14 sections • 23 lectures • 2h 52m total length
Video: MP4 1280x720 44 KHz | English + Sub
Updated 12/2021 | Size: 3.737 GB
Learn to build applied AI: Health care, Robotics, Chatbots, business analytics & misinformation detection applications

What you'll learn
Basics of Applied AI and fundamentals of deep learning in AI using Python (Keras and TensorFlow)
Applied AI codes for training and testing in Python for different applications
How to use Data Augmentation and Transfer Learning Techniques in Applied AI using Keras and TensorFlow
TensorFlow Quantum for training and testing of Hybrid Quantum Neural Networks (Python)
Requirements
No programming experience needed. You will learn everything you need to know
Description
AI is an enabler in transforming diverse realms by exploiting deep learning architectures.

The course aims to expose students to cutting-edge algorithms, techniques, and codes related to AI and particularly the deep learning routines. This course encompasses multidimensional implementations on the themes listed below;

1. Deep Learning: A subset of Hybrid Artificial Intelligence

2. Big Data is Fueling Applied AI.

3. How to model a problem in AI using datasets in Python (Keras & TensorFlow Libraries).

4. Data Augmentation in Hybrid Deep Learning Networks.

5. How to use Transfer Learning in Hybrid Deep Learning Networks.

6. How to use transfer learning in multiclass classification healthcare problems.

6. Backward Propagation and Optimization of hyper- parameters in AI.

7. Leading Convolutional Neural Networks (ALEXNET & INCEPTION) and validation indices.

8. Recurrent Neural Networks extending to Long Short Term Memory.

9. An understanding of Green AI.

10. Implementations of Neural Networks in Keras and Pytorch and introduction to Quantum Machine Learning.

11. Algorithms related to Quantum Machine Learning in TensorFlow Quantum and Qiskit.

12. AI based solutions for Neurological Diseases using Deep Learning.

13. AI for Brain Computer Interfacing and Neuromodulation.

14, AI algorithms for diagnosis, prognosis and treatment plans for Tumors.

15. How to model an AI problem in Healthcare.

16. AI in Block Chain and Crypto mining

17 AI in Crypto trading.

18. Forks in Block Chain via AI.

19. Investment Strategies in Crypto- trade using AI (Fungible and Non- Fungible Digital Currencies).

24. Artificial Intelligence in Robotics- A case example with complete code.

25. Artificial Intelligence in Smart Chatbots- A case example with complete code.

26. Impact of AI in business analytics- A case example with complete code.

27. AI in media and creative industries- A case example with complete code.

28. AI based advertisements for maximum clicks- A case example with complete code.

29. AI for the detection of Misinformation Detection.

30. Extraction of Fashion Trends using AI.

31. AI for emotion detections during Covid- 19.

Who this course is for
Beginner students curious about learning concepts of artificial intelligence and deep learning in python
Academic and Research Students working in the realm machine learning, deep neural networks and artificial intelligence

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