Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play 1st Edition

Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play 1st Edition

Description:
Generative Deep Learning by David Foster is a cutting-edge guide to one of the most fascinating areas of artificial intelligence (AI): teaching machines to create. Whether it’s painting, writing, or composing music, this book provides an in-depth understanding of how to build powerful generative models that can perform these human-like tasks. With practical insights and real-world examples, this book is an essential resource for machine-learning engineers and data scientists eager to explore the field of generative AI.

This book covers a range of advanced AI models such as variational autoencoders (VAEs), generative adversarial networks (GANs), encoder-decoder models, and world models. Starting with deep learning basics, the author gradually advances to the most state-of-the-art algorithms in the field. Whether you’re building GANs for music generation or experimenting with transformer models like BERT and GPT-2, Generative Deep Learning will provide you with all the tools needed to succeed.

Key Features:

• Explore Variational Autoencoders (VAEs): Learn how to use VAEs to manipulate and change facial expressions in photos.
• Build Generative Adversarial Networks (GANs): Understand how to create practical GANs from scratch, including CycleGAN for style transfer and MuseGAN for music generation.
• Create Text Generation Models: Develop recurrent generative models for text, and improve performance using attention mechanisms.
• Generative Models in Reinforcement Learning: Discover how these models can help AI agents accomplish tasks in reinforcement learning environments.
• Master Cutting-Edge Models: Dive into transformer architectures such as BERT and GPT-2, and image generation models like ProGAN and StyleGAN.

Table of Contents (Short Version)

1. Introduction to Generative Modeling
2. Variational Autoencoders (VAEs)
3. Building GANs from Scratch
4. CycleGAN for Style Transfer
5. MuseGAN for Music Generation
6. Recurrent Generative Models for Text
7. Attention Mechanisms for Text Generation
8. Generative Models in Reinforcement Learning
9. ProGAN and StyleGAN
10. Transformer Architectures: BERT and GPT-2

What Is Wrong with Deep Learning for Guided Tree Search?

One issue with deep learning in the context of guided tree search is its inefficiency in handling combinatorial explosion—a situation where the number of possible states or paths grows exponentially. Deep learning models often struggle with efficiently pruning the search space, resulting in high computational costs. Additionally, these models may not generalize well to unseen parts of the tree, leading to poor decision-making during the search process. This limitation makes deep learning less suitable for certain applications in tree-based search algorithms.

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Product Details:

• Publisher: O’Reilly Media; 2nd edition (June 6, 2023)
• ISBN-13: 9781098134181
• Language: English
• Print Length: 426 pages
• Format Type: PDF