Generative Deep Learning
David Foster is the co-founder of Applied Data Science, a data science company that provides clients with customized solutions. He has an MA in Mathematics from Trinity College in Cambridge, UK, as well as an MSc in Operational Research from the University of Warwick.
One of the trendiest subjects in AI is generative modeling. It is now possible to teach a machine to excel at human activities such as painting, writing, and music composition. Machine-learning engineers and data scientists will learn how to recreate some of the most spectacular instances of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models, using this practical book.
Author David Foster illustrates the inner workings of each technique, beginning with the fundamentals of deep learning and progressing to some of the field's most cutting-edge algorithms. In Generative Deep Learning, one of the best books on computer vision, you'll learn how to help your models learn more efficiently and creatively by using tips and tricks.
- Learn how variational autoencoders can alter facial emotions in photographs.
- Create your own practical GAN examples, such as CycleGAN for style transfer and MuseGAN for music production.
- Learn how to improve recurrent generative models for text production using attention.
- Learn how generative models can assist agents in completing tasks in a reinforcement learning scenario.
- Learn how computer vision recognizing and processing objects in visual inputs
- Investigate the Transformer architecture (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN.
Author: David Foster
Link to buy: https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1492041947/
Ratings: 4.5 out of 5 stars (from 180 reviews)
Best Sellers Rank: #131,948 in Books
#17 in Computer Vision & Pattern Recognition
#27 in Machine Theory (Books)
#47 in Artificial Intelligence (Books)
