Generative Deep Learning - by David Foster (Paperback)
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<p/><br></br><p><b> About the Book </b></p></br></br>"Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative."--Amazon.com.<p/><br></br><p><b> Book Synopsis </b></p></br></br><p>Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models.</p><p>Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative.</p><ul><li>Discover how variational autoencoders can change facial expressions in photos</li><li>Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation</li><li>Create recurrent generative models for text generation and learn how to improve the models using attention</li><li>Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting</li><li>Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN</li></ul><p/><br></br><p><b> About the Author </b></p></br></br><p>David Foster is the co-founder of Applied Data Science, a data science consultancy delivering bespoke solutions for clients. He holds an MA in Mathematics from Trinity College, Cambridge, UK and an MSc in Operational Research from the University of Warwick.</p><p>David has won several international machine learning competitions, including the Innocentive Predicting Product Purchase challenge and was awarded first prize for a visualisation that enables a pharmaceutical company in the US to optimize site selection for clinical trials.</p><p>He is an active participant in the online data science community and has authored several successful blog posts on deep reinforcement learning including 'How To Build Your Own AlphaZero AI'.</p>
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