--- product_id: 490359791 title: "Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play" price: "MX$2745" currency: MXN in_stock: true reviews_count: 13 url: https://www.desertcart.mx/products/490359791-generative-deep-learning-teaching-machines-to-paint-write-compose-play store_origin: MX region: Mexico --- # Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play **Price:** MX$2745 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play - **How much does it cost?** MX$2745 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.mx](https://www.desertcart.mx/products/490359791-generative-deep-learning-teaching-machines-to-paint-write-compose-play) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage. Review: Excellent review of types of deep learning models for generative tasks - In 2019 I bought, read and thoroughly loved the first edition of this book. One reason I loved that edition was the author’s excellent way of explaining generative adversarial networks (GAN), with humorous and relevant examples. At that point I was a lot more naive about the various deep learning models (ANN, RNN, CNN etc) and for a while I was unable to see where GANs fit in in the grand scheme or evolution of deep learning models. With the explosion in interest in generative AI after the release of ChatGPT-3, I read “Natural Language Processing with Transformers” by Turnstall etc. to get an understanding of the Transformer model. Along the way I read other sources of information on language models such as a paper “Survey of Large Language Models” by Socher etc. That later paper gave an excellent overview of the evolution of language models (statistical language models --> neural language models --> pretrained language models —> large language models). I also saw where language models fit in in the context of ANNs, RNN, CNN etc. When I saw that the author released a second edition of “Generative Deep Learning”, I noticed that the content had changed (ie increased) from the first edition, and I immediately decided to buy the second edition. This second edition has an excellent overview of the evolution of generative models, in fact 6 of them (variational auto encoders (VAE) —> generative adversarial networks (GAN) —> autoregressive models —> normalizing flow models —> energy based models —> diffusion models). I had never heard of some of these models. According to this author the Transformer is an application of generative deep learning models. The author goes on to describe other applications such as music generation and multimodal models. While this book requires one to know Python programming and offers code on GitHub, I was able to skip running the code and still learn a lot about generative deep learning. (I tried to run the code examples but couldn’t get around to it. I wish the author provided easier Jupyter notebooks for running the code). Another aspect of the book that I loved was the author’s description of key concepts like “probabilistic” versus “deterministic”, “discriminative” versus “generative” etc. I highly recommend this book as a great resource for a historical overview of generative deep learning. One should read it before one reads anything on just the transformer or language models. Review: First of all - Hello fellas, I ordered a generative deep learning book from an another source, but had poor quality as well as poor content. But this book is focused on an important part of generative deep learning also has a good quality of content. Really appreciate that I bought this book. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #119,418 in Books ( See Top 100 in Books ) #13 in Machine Theory (Books) #32 in Computer Neural Networks #33 in Natural Language Processing (Books) | | Customer Reviews | 4.4 out of 5 stars 185 Reviews | ## Images ![Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play - Image 1](https://m.media-amazon.com/images/I/81XMJ+7BbGL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Excellent review of types of deep learning models for generative tasks *by S***A on May 30, 2024* In 2019 I bought, read and thoroughly loved the first edition of this book. One reason I loved that edition was the author’s excellent way of explaining generative adversarial networks (GAN), with humorous and relevant examples. At that point I was a lot more naive about the various deep learning models (ANN, RNN, CNN etc) and for a while I was unable to see where GANs fit in in the grand scheme or evolution of deep learning models. With the explosion in interest in generative AI after the release of ChatGPT-3, I read “Natural Language Processing with Transformers” by Turnstall etc. to get an understanding of the Transformer model. Along the way I read other sources of information on language models such as a paper “Survey of Large Language Models” by Socher etc. That later paper gave an excellent overview of the evolution of language models (statistical language models --> neural language models --> pretrained language models —> large language models). I also saw where language models fit in in the context of ANNs, RNN, CNN etc. When I saw that the author released a second edition of “Generative Deep Learning”, I noticed that the content had changed (ie increased) from the first edition, and I immediately decided to buy the second edition. This second edition has an excellent overview of the evolution of generative models, in fact 6 of them (variational auto encoders (VAE) —> generative adversarial networks (GAN) —> autoregressive models —> normalizing flow models —> energy based models —> diffusion models). I had never heard of some of these models. According to this author the Transformer is an application of generative deep learning models. The author goes on to describe other applications such as music generation and multimodal models. While this book requires one to know Python programming and offers code on GitHub, I was able to skip running the code and still learn a lot about generative deep learning. (I tried to run the code examples but couldn’t get around to it. I wish the author provided easier Jupyter notebooks for running the code). Another aspect of the book that I loved was the author’s description of key concepts like “probabilistic” versus “deterministic”, “discriminative” versus “generative” etc. I highly recommend this book as a great resource for a historical overview of generative deep learning. One should read it before one reads anything on just the transformer or language models. ### ⭐⭐⭐⭐⭐ First of all *by P***. on July 29, 2023* Hello fellas, I ordered a generative deep learning book from an another source, but had poor quality as well as poor content. But this book is focused on an important part of generative deep learning also has a good quality of content. Really appreciate that I bought this book. ### ⭐⭐⭐⭐⭐ The book I was looking for *by R***T on June 9, 2023* Amazing book, for me the best thing about it is that there are many well-sourced and working code and data examples which are explained clearly in the text. ## Frequently Bought Together - Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play - Natural Language Processing with Transformers, Revised Edition - Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.mx/products/490359791-generative-deep-learning-teaching-machines-to-paint-write-compose-play](https://www.desertcart.mx/products/490359791-generative-deep-learning-teaching-machines-to-paint-write-compose-play) --- *Product available on Desertcart Mexico* *Store origin: MX* *Last updated: 2026-05-19*