Generative Adversarial Networks Archives

GANs in Action: Deep learning with Generative Adversarial Networks by Jakub Langr and Vladimir Bok. Vladimir is a Data Product Manager at Intent. In addition, I really welcome his statement: Why I Donate All of My Book’s Proceeds to Girls Who Code. Jakub is Co-Found of Hypermile, a UK startup deploying AI across transportation solutions.

GANs in Action by Jakub Langr and Vladimir Bok

Jakub and Vladimir have certainly written a wonderful book on machine learning algorithms that generate realistic imaging. However, this book is really intended for readers who already have some experience with machine learning and neural networks.

Whereas many consumers view new imaging services as a kind of magic, super computing power delivered by AI. There are indeed large machine learning datasets in play that even make this imaging possible.

GANs in Action is a very worthy followup to John Kelleher’s Machine Learning, Melanie Mitchell’s excellent book Artificial Intelligence: A Guide for Thinking Humans, and Sean Gerrish’s How Smart Machines Think. Each author is in fact, addressing in various scales, the introduction to Neural Networks and GANs. Thankfully, Jakub and Vladimir have taken the necessary next step in delivering a wonderful introduction and coding deep dive to GANs.

In fact, for many consumers the Grokking series of books are a must read. Grokking Algorithms: An Illustrated Guide For Programmers and Other Curious People by Aditya Bhargava and Grokking Artificial Intelligence Algorithms: Understand and apply the core algorithms of deep learning and artificial intelligence by Rishal Hurbans. Thus, both are wonderfully illustrated books to begin anyone’s journey into understanding Artificial Intelligence, Machine Learning, and Deep Learning.

D eep Learning by John D. Kelleher. John is the Academic Leader of the Information, Communication and Entertainment research institute at the Technological University Dublin. He has previously taught at Dublin City University, Media Lab Europe, and DFKI (the German Centre for Artificial Intelligence Research).

This is a very good introduction to specific subsets of artificial intelligence that are indeed powering imaging, speech recognition, machine translation, and autonomous cars today.

Consumers may forget as they are engaging various technologies, their interactions are via Deep Learning systems. This includes interactions with Siri on iPhones, and Alexa on all things from Amazon. To a lesser extent is Cortana from Microsoft. Actually, John provides a wonderful glossary. This serves the reader well in helping to further develop their understanding of Deep Learning systems.

Likewise, his introduction illustrates how Deep learning delivers data-driven decisions from very large datasets. The key is Deep Learning deliver immediate ‘learning’ as the large datasets grow.

In addition, his insights on autoencoders, recurrent neural networks, and Generative Adversarial Networks (GAN) are very stimulating. At the same time, addressing gradient descent and especially backpropagation is amazing in of itself.