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Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play
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Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play

Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play

David Foster

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360 pages, parution le 29/06/2023
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Résumé

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 (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy based models, and diffusion models.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 (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy based models, and diffusion 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. Discover how VAEs can change facial expressions in photos Build practical GAN examples from scratch to generate images based on your own dataset Create autoregressive generative models, such as LSTMs for text generation and PixelCNN models for image generation Build music generation models, using Transformers and MuseGAN Explore the inner workings of state-of-the-art architectures such as StyleGANGPT-3, and DDIM Dive into the the detail of multimodal models such as DALL.E 2 and Imagen for text-to-image generation Understand how generative world models can help agents accomplish tasks within a reinforcement learning setting Understand how the future of generative modeling might evolve, including how businesses will need to adapt to take advantage of the new technologiesDavid Foster is a Founding Partner of ADSP, a consultancy delivering bespoke data science and AI solutions. He holds an MA in Mathematics from Trinity College, Cambridge and an MSc in Operational Research from the University of Warwick. Through ADSP, David leads the delivery of high-profile data science and AI projects across the public and private sectors. He has won several international machine-learning competitions, including the Innocentive Predicting Product Purchase challenge and for delivering a process to enable a pharmaceutical company in the US to optimize site selection for clinical trials. He is a member of the Machine Learning Institute Faculty and has given talks internationally on topics related to the application of cutting-edge data science and AI within industry and academia.

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Éditeur(s) O'Reilly
Auteur(s) David Foster
Parution 29/06/2023
Nb. de pages 360
EAN13 9781098134181

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