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. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing Atari with Deep Reinforcement Learning. arXiv. https://arxiv.org/abs/1312.5602
  2. Bareketain, P. (2019, March 10). Understanding Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. Medium. https://medium.com/@parnianbrk/understanding-stabilising-experience-replay-for-deep-multi-agent-reinforcement-learning-84b4c04886b5
  3. Wikipedia user waldoalverez, under a CC BY-SA 4.0 license (https://creativecommons.org/licenses/by-sa/4.0/).
  4. Amit, R., Meir, R., & Ciosek, K. (2020). Discount Factor as a Regularizer in Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020. http://proceedings.mlr.press/v119/amit20a/amit20a.pdf
  5. Matiisen, T. (2015, December 19). Demystifying Deep Reinforcement Learning. Computational Neuroscience Lab. https://neuro.cs...