--- product_id: 542829403 title: "Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play" price: "SAR 329" currency: SAR in_stock: true reviews_count: 5 url: https://www.desertcart.com.sa/products/542829403-generative-deep-learning-teaching-machines-to-paint-write-compose-play store_origin: SA region: Saudi Arabia --- # Comprehensive deep learning guide Cutting-edge architectures & techniques Hands-on generative AI models Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play **Price:** SAR 329 **Availability:** ✅ In Stock ## Summary > 🤖 Unlock the future of creativity with AI-powered innovation! ## Quick Answers - **What is this?** Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play - **How much does it cost?** SAR 329 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.com.sa](https://www.desertcart.com.sa/products/542829403-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 ## Key Features - • **Build Creative AI Models:** Learn to generate images, text, and music with state-of-the-art tools like StyleGAN2 and MuseGAN. - • **Future-Proof Your Skills:** Explore how generative AI drives innovation and competitive advantage in tech industries. - • **Deep Dive into Multimodal AI:** Understand and create models like DALL·E 2 and Stable Diffusion that blend vision and language. - • **Master Generative AI from Scratch:** Step-by-step tutorials on VAEs, GANs, Transformers, and diffusion models. - • **Practical TensorFlow & Keras Applications:** Implement real-world models with popular frameworks used by professionals. ## Overview Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play is a 2023 paperback that equips machine learning engineers and data scientists with practical skills to build advanced generative AI models using TensorFlow and Keras. Covering everything from foundational deep learning to cutting-edge architectures like VAEs, GANs, Transformers, and diffusion models, this book empowers readers to create AI that generates images, text, and music. With a strong focus on real-world applications and future trends, it’s a must-have resource for professionals aiming to lead in the AI-driven creative revolution. ## 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: A superb, practical book - An excellent, practical book for deep learning practitioners. Review: Lack of a critical aspect - Although the book covers many key techniques in generative AI, a key question needs to be answered, how do we know if it's generating a good quality image other than by eyeballing it? There should be a section that talks about the joint use of the discriminative model and generative model, for example, if we were using the generative model to augment the dataset for the downstream discriminative task (image classification), how do we evaluate the generated data has been helpful, some may say just look at the performance difference of downstream task, but I bet there is more insight than that, author need to consider this problem in future edition. ## Features - Binding: paperback - Language: english - Publication date: 2023-05-12T00:00:00.000Z ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #94,292 in Books ( See Top 100 in Books ) #137 in Databases & Big Data #179 in Computer Software #634 in Computer Science | | Customer Reviews | 4.6 out of 5 stars 152 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 ### ⭐⭐⭐⭐⭐ A superb, practical book *by S***U on 26 March 2026* An excellent, practical book for deep learning practitioners. ### ⭐⭐⭐ Lack of a critical aspect *by R***N on 22 July 2023* Although the book covers many key techniques in generative AI, a key question needs to be answered, how do we know if it's generating a good quality image other than by eyeballing it? There should be a section that talks about the joint use of the discriminative model and generative model, for example, if we were using the generative model to augment the dataset for the downstream discriminative task (image classification), how do we evaluate the generated data has been helpful, some may say just look at the performance difference of downstream task, but I bet there is more insight than that, author need to consider this problem in future edition. ### ⭐⭐⭐⭐⭐ Excellent review of types of deep learning models for generative tasks *by S***A on 30 May 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. ## 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.com.sa/products/542829403-generative-deep-learning-teaching-machines-to-paint-write-compose-play](https://www.desertcart.com.sa/products/542829403-generative-deep-learning-teaching-machines-to-paint-write-compose-play) --- *Product available on Desertcart Saudi Arabia* *Store origin: SA* *Last updated: 2026-08-21*