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Generative AI with Python and TensorFlow 2

Generative AI with Python and TensorFlow 2

By : Joseph Babcock, Raghav Bali
4.4 (27)
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Generative AI with Python and TensorFlow 2

Generative AI with Python and TensorFlow 2

4.4 (27)
By: Joseph Babcock, Raghav Bali

Overview of this book

Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.
Table of Contents (16 chapters)
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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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