Deep Learning Step By Step Guide For Students Entrepreneurs Business Leaders & The Curious

[FaridKhan]

English | April 23, 2024 | ASIN: B0D2KL8732 | 182 pages | PDF | 2.01 Mb

Demystifying Deep Learning: Your Step-by-Step Guide to AI's Most Powerful Technology!
Unlock the potential of deep learning and join the AI revolution with this comprehensive and accessible guide, perfect for students and beginners, entrepreneurs, business leaders, and curious minds alike.

" Deep Learning Step by Step" takes you on a journey from the mechanics, foundation, and concepts of neural networks to cutting-edge applications transforming industries like healthcare, finance, and manufacturing.

Here's what you'll discover:

✔The Core Concepts: Understand the mechanics, foundations, and concepts behind deep learning, from neurons and activation functions to various network architectures like CNNs and RNNs.
✔Data as the Foundation: Learn how to collect, prepare, and augment data to fuel your deep learning models for optimal performance.
✔Training Secrets Revealed: Explore optimization algorithms, regularization techniques, and best practices for training models.
✔Real-World Applications: Image recognition, natural language processing (NLP), speech recognition & more.
✔Business Applications: Enhance customer experience & optimize operations.
✔Ethical Considerations: Data privacy, algorithmic bias, and the societal impact of AI.
✔Building a Career in AI: Explore emerging roles in deep learning and acquire essential skills (e.g., Python, PyTorch).
✔Frontiers of Deep Learning: Trends like self-supervised learning, agentic models, and quantum computing.

Concepts & Topics Covered
1. Deep Learning Fundamentals:

✔Neural networks
✔Neurons, weights and biases
✔Activation functions (Sigmoid, Tanh, ReLU)
✔Network architectures (CNNs, RNNs, LSTMs)
✔Learning processes (gradient descent, backpropagation)
✔Depth and complexity
✔Overfitting and underfitting
✔Regularization techniques
✔Cross-validation
✔Optimization algorithms (SGD, Adam, RMSprop)
✔Frameworks (e.g., Python, PyTorch, TensorFlow)

2. Data for Deep Learning:

✔Data quality
✔Data collection strategies
✔Datasets (MNIST, CIFAR, ImageNet)
✔Data augmentation
✔Large-scale data management

3. Deep Learning Applications:

✔Image recognition and classification
✔Object detection
✔Natural Language Processing (NLP)
✔Sentiment analysis
✔Machine translation
✔Speech recognition
✔Music generation
✔Time series forecasting
✔Generative Adversarial Networks (GANs)
✔Reinforcement Learning (RL)

4. Deep Learning in Industries:

✔Healthcare (diagnostics, personalized medicine)
✔Finance (algorithmic trading, asset management)
✔Retail and E-commerce (recommendation systems, personalization)
✔Manufacturing and Supply Chain
✔Internet of Things (IoT) (smart devices, energy management, security)

5. Deep Learning Ethics & Society:

✔Data privacy and security
✔Bias and fairness
✔Misinformation and deepfakes
✔Surveillance and privacy concerns
✔Social implications of AI
✔AI governance and regulation
✔Explainable AI (XAI)
✔Deep learning for social good

6. Career & Business Aspects:

✔Emerging roles in deep learning
✔Skills for deep learning careers
✔Deep learning impact on jobs
✔Monetizing deep learning (licensing, AIaaS)
✔Funding deep learning startups
✔Deep learning and corporate strategy
✔Marketing deep learning innovations

7. Future of Deep Learning:

✔Trends & emerging technologies
✔Quantum computing and AI
✔Next-generation AI models and agentic deep learning
✔Sub-fields beyond deep learning

Contents of Download:
B0D2KL8732.pdf (Marcus Vinicius Pinto) (2024) (2.01 MB)

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️ Deep Learning Step By Step Guide For Students Entrepreneurs Business Leaders & The Curious (2.01 MB)
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