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
Other Books You May Enjoy
15
Index

References

  1. LeCun, Yann; Léon Bottou; Yoshua Bengio; Patrick Haffner (1998). Gradient- Based Learning Applied to Document Recognition. Proceedings of the IEEE. 86 (11): 2278–2324
  2. LeCun, Yann; Corinna Cortes; Christopher J.C. Burges. MNIST handwritten digit database, Yann LeCun, Corinna Cortes, and Chris Burges
  3. NIST's original datasets: https://www.nist.gov/system/files/documents/srd/nistsd19.pdf
  4. https://upload.wikimedia.org/wikipedia/commons/thumb/2/27/MnistExamples.png/440px-MnistExamples.png
  5. LeCun, Yann; Léon Bottou; Yoshua Bengio; Patrick Haffner (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE. 86 (11): 2278–2324
  6. D. Ciregan, U. Meier and J. Schmidhuber, (2012) Multi-column deep neural networks for image classification, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642-3649. https://ieeexplore.ieee.org/document/6248110
  7. Hinton GE, Osindero S, Teh...