The best book to learn about Generative Deep Learning: one of the hottest fields of Artificial Intelligence
Generative Deep Learning: The following is a review of Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster.
Review of Generative Deep Learning
Generative Deep Learning is a branch of Machine Learning that in the recent years has generated a lot of hype, and also eye-opening outputs. It is the core of technologies like Generative Adversarial Networks (GANs) that generate the DeepFakes we’ve all heard about, and other awesome feats like animated paintings. Check out this animated Mona Lisa created using generative deep learning.
GANs were introduced in 2014 by Ian Goodfellow, one of the authors of Deep Learning; the most theoretically comprehensive Deep Learning book out there, and since then have gained a lot of attention, building on top of these Generative Deep Neural Networks.
These models use on Deep Artificial Neural Networks to create their outputs, and they are becoming increasingly used in art generation, text generation with models like GPT-3, and even in Reinforcement Learning, having agents simulate or ‘imagine’ the future through generative systems.
Understanding how Generative Deep Neural Net systems are created, and how they work under the hood is not so easy, as there is a lot of sparse material on Medium Articles, or Youtube Videos that cover the topic superficially but don’t dive into the guts of the algorithms. With the Generative Deep Learning book we have found the best, most efficient and organised way to learn about this topic. Lets see what it contains!
The contents of the book mix theoretical explanations, highly illustrative examples, and Python code, to get a full 360º view of the material that is being covered.
- Introduction to Generative Deep Learning: Generative Modeling defined, with its most famous examples and goals, along with explanations of the fundamental building blocks you need to understand in order to truly grasp Generative Deep Learning.
- What Is Generative Modeling?
- Deep Learning
- Variational Autoencoders
- Generative Adversarial Networks
- Teaching Machines to Paint, Write, Compose, and Play.
- Write: generating text, tokenization, and how RNN extensions build on top of Generative Networks. LSTM Layer
- Compose: using generative deep learning to create music, Recurrent Neural Networks and the MusicGAN generator.
- Play: The use of Generative Deep Learning in reinforcement learning for videogames. How the reinforcement learning systems that implement these algos are trained and used. Variational Auto Encoders.
- The Future of Generative Modeling: creation of images, music, and text using generative deep learning for art. Transformers with BERT and GPT-2 being covered.
- Conclusion: wrapping up and further resources.
As you can see the book packs a block with the explanation of the main concepts behind generative deep learning (deep learning, VAEs, etc..) and another block with explanations and examples of the areas where it is widely used.
This book assumes that you have experience coding in Python. If you are not familiar with Python.
Don’t worry if you are not familiar with it, we’ve got reviews of the best resources to get you up and running: Python Crash Course, Automating the Boring Stuff with Python, and Learning Python by Mark Lutz are the books we normally recommend to start with. Some other non-books that are great to start learning online are: the Kaggle Learn Python Tutorials, Learning Python Course on Codeacademy. and the Real Python Website.
Also, since some of the models are described using mathematical notation, it will be useful to have a solid understanding of linear algebra (for example, matrix multiplication, etc.) and general probability theory. In our Tutorials category you can learn all about these.
Summary of Generative Deep Learning
This book is the best single resources we have found to learn about this exciting field. It is well written, it has fantastic and illustrative examples, and it appeals to the readers intuition and imagination.
We recommend using a well set-up coding environment to have by your side while you read through the book in order to implement and modify the code examples available in Python and Keras.
It’s got good introductions to each popular dataset, contains useful code, is highly readable and refreshing, and uses equations sparingly and effectively, without dumbing down the content too much. However, there could be are some errors with the code examples provided and the library imports and code snippets, which might be fixed right now.
Overall, a great buck for your money, which highly we recommend if you want to seriusly get into this great branch of Machine Learning. Find it on amazon at the best price and learn what generative models in machine learning are and how they are built:
- Foster, David (Author)
- English (Publication Language)
- 330 Pages - 07/23/2019 (Publication Date) - O'Reilly Media (Publisher)
Find additional resources here:
- An Introduction to Deep Generative Model paper.
- If you are interested in Deep learning, and are looking for further resources, check our review of the Deep Learning Specialisation by Andrew Ng on Coursera
- Our Machine Learning books category is another great place to find the best deep learning books!
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Tags: Generative Models in Machine Learning, Machine Learning Generative Models, What is a Generative Deep Learning model.