Book Image

Generative AI with Python and TensorFlow 2

By : Joseph Babcock, Raghav Bali
4 (1)
Book Image

Generative AI with Python and TensorFlow 2

4 (1)
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)
14
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15
Index

Summary

In this chapter, we've covered the basic vocabulary of deep learning – how initial research into perceptrons and MLPs led to simple learning rules being abandoned for backpropagation. We also looked at specialized neural network architectures such as CNNs, based on the visual cortex, and recurrent networks, specialized for sequence modeling. Finally, we examined variants of the gradient descent algorithm proposed originally for backpropagation, which have advantages such as momentum, and described weight initialization schemes that place the parameters of the network in a range that is easier to navigate to a local minimum.

With this context in place, we are all set to dive into projects in generative modeling, beginning with the generation of MNIST digits using Deep Belief Networks in Chapter 4, Teaching Networks to Generate Digits.