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Generative AI with Python and TensorFlow 2

You're reading from  Generative AI with Python and TensorFlow 2

Product type Book
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Pages 488 pages
Edition 1st Edition
Languages
Authors (2):
Joseph Babcock Joseph Babcock
Profile icon Joseph Babcock
Raghav Bali Raghav Bali
Profile icon Raghav Bali
View More author details
Toc

Table of Contents (16) Chapters close

Preface 1. An Introduction to Generative AI: "Drawing" Data from Models 2. Setting Up a TensorFlow Lab 3. Building Blocks of Deep Neural Networks 4. Teaching Networks to Generate Digits 5. Painting Pictures with Neural Networks Using VAEs 6. Image Generation with GANs 7. Style Transfer with GANs 8. Deepfakes with GANs 9. The Rise of Methods for Text Generation 10. NLP 2.0: Using Transformers to Generate Text 11. Composing Music with Generative Models 12. Play Video Games with Generative AI: GAIL 13. Emerging Applications in Generative AI 14. Other Books You May Enjoy
15. Index

Perceptrons – a brain in a function

The simplest neural network architecture – the perceptron – was inspired by biological research to understand the basis of mental processing in an attempt to represent the function of the brain with mathematical formulae. In this section we will cover some of this early research and how it inspired what is now the field of deep learning and generative AI.

From tissues to TLUs

The recent popularity of AI algorithms might give the false impression that this field is new. Many recent models are based on discoveries made decades ago that have been reinvigorated by the massive computational resources available in the cloud and customized hardware for parallel matrix computations such as Graphical Processing Units (GPUs), Tensor Processing Units (TPUs), and Field Programmable Gate Array (FPGAs). If we consider research on neural networks to include their biological inspiration as well as computational theory, this field is...

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