Generative Deep Learning Updated Edition
Unlocking the Creative Power of Al and Python
Detailed insights into the latest AI advancements, including GANs, VAEs, Autoregressive and Diffusion models, through clear explanations, examples, and projects.
What You'll Get from This Book
10 chapters spanning over 520 pages
More than 250 explanatories blocks of code
More than 25 practical exercises
5 Quizzes to test your knowledge
4 Practical "Real World" Projects
Dive into the Future of AI with Generative Deep Learning
Unlock the vast potential of Artificial Intelligence with our comprehensive guide, "Generative Deep Learning Updated Edition: Unlocking the Creative Power of AI and Python". This comprehensive piece of literature serves as your definitive roadmap to mastering the most recent advancements within the rapidly evolving field of AI. It explores a wide range of innovative technologies including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Autoregressive Models, and the groundbreaking Diffusion Models.
Each chapter of this meticulously constructed guide is densely packed with clear, easy-to-understand explanations, paired with detailed examples that further illustrate these complex concepts. Moreover, it includes a series of hands-on, engaging projects specifically designed to provide practical knowledge and application of these cutting-edge techniques.
Our goal is to empower you with a deep understanding and proficiency in these advanced AI concepts, enabling you to unlock and harness the transformative power of AI and Python.
Transform Your AI Skills with Practical Projects and Real-World Applications
This book is a comprehensive guide that goes beyond just theory, offering readers the chance to engage with practical projects that breathe life into AI concepts. This hands-on approach allows you to delve deep into the world of AI, providing an immersive learning experience that is both enjoyable and educational.
The book offers opportunities to construct Generative Adversarial Networks (GANs), powerful algorithms that are used to generate incredibly realistic images. This practical application of theory will give you firsthand experience of how these complex algorithms work, and the results they can achieve.
In addition, you'll also have the chance to create Variational Autoencoders (VAEs), which are used for data augmentation. This gives you an insight into how AI can manipulate and enhance data to achieve better results, providing you with a deeper understanding of the field.
Furthermore, the book guides you through the process of developing autoregressive models for text generation. This gives you a glimpse into how AI can be used in content creation and language processing, broadening your understanding of the diverse applications of AI.
The "Generative Deep Learning Updated Edition" is meticulously crafted to offer both clarity and depth. Each chapter builds on the last, ensuring a smooth learning curve whether you're new to AI or an experienced practitioner.
The detailed explanations and hands-on projects are designed to help you master these complex topics and apply them confidently in your work.
Don't miss out on this opportunity to gain a comprehensive understanding of generative deep learning and unlock the creative power of AI and Python. Click the "Buy Now" button to secure your copy and join the AI revolution!
Table of contents
Chapter 1: Introduction to Deep Learning
1.1 Basics of Neural Networks
1.2 Overview of Deep Learning
1.3 Recent Advances in Deep Learning
1.4 Practical Exercises - Chapter 1: Introduction to Deep Learning
1.5 Chapter Summary - Chapter 1: Introduction to Deep Learning
Chapter 2: Understanding Generative Models
2.1 Concept and Importance
2.2 Delve Deeper into Types of Generative Models
2.3 Recent Developments in Generative Models
2.4 Practical Exercises - Chapter 2: Understanding Generative Models
2.5 Chapter 2 Summary - Chapter 2: Understanding Generative Models
Quiz: Foundations of Deep Learning
Questions of Chapter 1: Introduction to Deep Learning
Questions of Chapter 2: Understanding Generative Models
Answers - Quiz: Foundations of Deep Learning
Chapter 3: Deep Dive into Generative Adversarial Networks (GANs)
3.1 Understanding GANs
3.2 Architecture of GANs
3.3 Training GANs
3.4 Evaluating GANs
3.5 Variations of GANs
Chapter 4: Project Face Generation with GANs
4.1 Data Collection and Preprocessing
4.2 Model Creation
4.3 Training the GAN
4.4 Generating New Faces
4.5 Evaluating the Model
Quiz: Generative Adversarial Networks (GAN)
Questions - Chapter 3: Deep Dive into Generative Adversarial Networks (GANs)
Questions - Chapter 4: Project: Face Generation with GANs
Answers - Quiz: Generative Adversarial Networks (GAN)
Chapter 5: Exploring Variational Autoencoders (VAEs)
5.1 Understanding VAEs
5.2 Architecture of VAEs
5.3 Training VAEs
5.4 Evaluating VAEs
5.5 Variations of VAEs (Beta-VAE, Conditional VAE)
Chapter 6: Project: Handwritten Digit Generation with VAEs
6.1 Data Collection and Preprocessing
6.2 Model Creation
6.3 Generating New Handwritten Digits
6.4 Evaluating the Model
6.5 Enhancing Digit Generation with Beta-VAE
Quiz: Variational Autoencoders (VAEs)
Questions - Quiz: Variational Autoencoders (VAEs)
Answers - Quiz: Variational Autoencoders (VAEs)
Chapter 7: Understanding Autoregressive Models
7.1 PixelRNN and PixelCNN
7.2 Transformer-based Models (GPT, GPT-3, GPT-4)
7.3 Use Cases and Applications of Autoregressive Models
7.4 Practical Exercises - Chapter 7: Understanding Autoregressive Models
7.5 Chapter Summary - Chapter 7: Understanding Autoregressive Models
Chapter 8: Project: Text Generation with Autoregressive Models
8.1 Data Collection and Preprocessing
8.2 Model Creation
8.3 Generating Text with the Fine-Tuned Model
8.4 Evaluating the Model
Quiz: Autoregressive Models
Questions - Quiz: Autoregressive Models
Answers - Quiz: Autoregressive Models
Chapter 9: Exploring Diffusion Models
9.1 Understanding Diffusion Models
9.2 Architecture of Diffusion Models
9.3 Training Diffusion Models
9.4 Evaluating Diffusion Models
9.5 Practical Exercises - Chapter 9: Exploring Diffusion Models
Chapter 10: Project: Image Generation with Diffusion Models
10.1 Data Collection and Preprocessing
10.2 Model Creation
10.3 Training the Diffusion Model
10.4 Generating New Images
10.5 Evaluating the Model
Quiz: Diffusion Models of the book which includes
Questions - Quiz: Diffusion Models of the book which includes
Answers - Quiz: Diffusion Models of the book which includes
What our readers are saying about this book
Explore the reviews to understand why this book is a great choice! Discover how others have gained from the knowledge and insights it provides. Get a taste of the exciting content that awaits you and see if this book is the perfect fit for your journey.
I've read many books on AI, but 'Generative Deep Learning Updated Edition' stands out for its depth and clarity. The detailed explanations of GANs, VAEs, and diffusion models are not only easy to follow but also immensely practical. The hands-on projects are a fantastic way to reinforce the concepts and see real-world applications. This book has significantly improved my understanding of generative models and their potential. Highly recommended for anyone serious about mastering AI and Python!
The updated edition brings the latest advancements in generative deep learning right to your fingertips. The authors do a great job of breaking down complex topics into manageable sections, complete with practical exercises and projects. Whether you're a beginner or an experienced practitioner, this book is an invaluable resource. It's thorough, well-structured, and incredibly engaging. Kudos to the authors for such a fantastic work!
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