Harness the power of AI with this guide to using Keras! Start by reviewing the fundamentals of deep learning and installing the Keras API. Next, follow Python code examples to build your own models, and then train them using classification, gradient descent, and regularization. Design large-scale, multilayer models and improve their decision making with reinforcement learning. With tips for creating generative AI models, this is your cutting-edge resource for working with deep learning!

Highlights include:
1) Neural networks
2) Gradient descent
3) Classification
4) Regularization
5) Convolutional neural networks (CNNs)
6) Functional API
7) Transformer architecture
8) Reinforcement learning
9) Autoencoders
10) Stable Diffusion

Build and train convolutional neural networks, transformers, and autoencoders

Autorentext
Dr. Mohammad Nauman has a post-doctorate degree from Max Planck Institute for Software Systems, Germany. He is a seasoned machine learning expert with more than 20 years of teaching experience and a track record of educating 40,000+ students globally through his paid and free online courses on platforms like Udemy and YouTube. He holds a PhD in computer science, with his groundbreaking work at the Max Planck Institute focusing on applying machine learning to advance security and privacy solutions. Dr. Nauman's teaching philosophyrooted in bridging theory and practiceempowers learners to master tools while building robust foundational skills, whether in academic settings or through his widely accessible digital programs.

Inhalt
1 ... Introduction ... 17

1.1 ... Overview of Deep Learning ... 18

1.2 ... Why Keras ... 23

1.3 ... The Structure of This Book ... 25

1.4 ... How to Use This Book ... 28

2 ... Introduction to the Core of Machine Learning ... 33

2.1 ... What Is Machine Learning? ... 35

2.2 ... Types of Machine Learning ... 49

2.3 ... The Magic Sauce: Reinforcement Learning ... 65

2.4 ... Basics of Neural Networks ... 69

2.5 ... Setting Up Your Environment ... 73

2.6 ... Summary ... 78

3 ... Fundamentals of Gradient Descent ... 79

3.1 ... Understanding Gradient Descent ... 80

3.2 ... Types of Gradient Descent: Batch, Stochastic, Mini-Batch ... 101

3.3 ... Learning Rate and Optimization ... 107

3.4 ... Implementing Gradient Descent in Code ... 110

3.5 ... Summary ... 116

4 ... Classification Through Gradient Descent ... 117

4.1 ... Classification Basics ... 118

4.2 ... Nonlinear Relationships and Neural Networks ... 136

4.3 ... Binary vs. Multi-Class Classification ... 147

4.4 ... Loss Functions: Cross-Entropy ... 155

4.5 ... Building a Classifier with Gradient Descent ... 161

4.6 ... Summary ... 166

5 ... Deep Dive into Keras ... 167

5.1 ... Introduction to Keras Framework ... 168

5.2 ... Setting Up Keras ... 174

5.3 ... Building Your First Model ... 188

5.4 ... Implementing Core Concepts in Keras: Gradient Descent and Classification ... 205

5.5 ... Summary ... 222

6 ... Regularization Techniques ... 223

6.1 ... An Overview of Overfitting and Underfitting: Do You Need More Data? ... 224

6.2 ... Dropout: Concept and Implementation ... 243

6.3 ... Other Regularization Methods: L1 and L2 Regularization ... 251

6.4 ... Applying Regularization in Keras ... 254

6.5 ... Summary ... 264

7 ... Convolutional Neural Networks ... 265

7.1 ... Introduction to Convolutional Neural Networks ... 266

7.2 ... Convolutional Layers, Pooling Layers and Fully Connected Layers ... 287

7.3 ... Implementing CNNs with Keras ... 301

7.4 ... The Shapes Problem ... 303

7.5 ... Case Study: Image Classification ... 307

7.6 ... Summary ... 313

8 ... Exploring the Keras Functional API ... 315

8.1 ... Overview of Keras Functional API ... 316

8.2 ... Building Complex Models with the Functional API ... 323

8.3 ... Use Cases and Examples ... 340

8.4 ... Using Transfer Learning to Customize Models for Your Organization ... 364

8.5 ... Summary ... 373

9 ... Understanding Transformers ... 375

9.1 ... The Theory Behind Transformers ... 376

9.2 ... Components: Attention Mechanism, Encoder, Decoder ... 393

9.3 ... Implementing Transformers in Keras ... 406

9.4 ... Case Study: Large Language Model Chatbot ... 418

9.5 ... Summary ... 427

10 ... Reinforcement Learning: The Secret Sauce ... 429

10.1 ... Introduction to Reinforcement Learning ... 430

10.2 ... Key Concepts: Agents, Environments, Rewards ... 438

10.3 ... Popular Algorithms: Q-Learning, Policy Gradients, and Deep Q-Networks ... 447

10.4 ... Implementing Reinforcement Learning Models in Keras ... 464

10.5 ... Reinforcement Learning in Large Language Models ... 486

10.6 ... Summary ... 493

11 ... Autoencoders and Generative AI ... 495

11.1 ... Introduction to Autoencoders ... 496

11.2 ... Variational Autoencoders ... 519

11.3 ... Generative Adversarial Networks ... 535

11.4 ... Summary ... 552

12 ... Advanced Generative AI: Stable Diffusion ... 553

12.1 ... Theory Behind Stable Diffusion ... 554

12.2 ... How Stable Diffusion Uses Core Concepts ... 565

12.3 ... Implementing Stable Diffusion Models ... 572

12.4 ... Case Study: Image Generation ... 593

12.5 ... Summary ... 603

13 ... Recap of Key Concepts ... 605

13.1 ... Future Trends in Deep Learning ... 606

13.2 ... Tips for Staying Updated with Advancements ... 611

13.3 ... Following the Latest Research ... 615

... The Author ... 619

... Index ... 621