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Generative AI with Python and TensorFlow 2

You're reading from   Generative AI with Python and TensorFlow 2 Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Length 488 pages
Edition 1st Edition
Languages
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Authors (2):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Joseph Babcock Joseph Babcock
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Joseph Babcock
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Toc

Table of Contents (16) Chapters Close

Preface 1. An Introduction to Generative AI: "Drawing" Data from Models 2. Setting Up a TensorFlow Lab FREE CHAPTER 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

References

  1. Abadi, Martín, et al. (2016) TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv:1603.04467. https://arxiv.org/abs/1603.04467.
  2. Google. TensorFlow. Retrieved April 26, 2021, from https://www.tensorflow.org/
  3. MATLAB, Natick, Massachusetts: The MathWorks Inc. https://www.mathworks.com/products/matlab.html
  4. Krizhevsky A., Sutskever I., & Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks. https://papers.nips.cc/paper/4824-imagenet-classification-with-deepconvolutional-neural-networks.pdf
  5. Dean J., Ng A., (2012, Jun 26). Using large-scale brain simulations for machine learning and A.I.. Google | The Keyword. https://blog.google/technology/ai/using-large-scale-brain-simulations-for/
  6. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M. (2013). Playing Atari with Deep Reinforcement Learning. arXiv:1312.5602. https://arxiv.org/abs/1312.5602
  7. Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A, Chen Y, Lillicrap T, Hui F, Sifre L, van den Driessche G, Graepel T, Hassabis D. (2017) Mastering the game of Go without human knowledge. Nature. 550(7676):354-359. https://pubmed.ncbi.nlm.nih.gov/29052630/
  8. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. https://arxiv.org/abs/1810.04805
  9. Al-Rfou, R., et al. (2016). Theano: A Python framework for fast computation of mathematical expressions. arXiv. https://arxiv.org/pdf/1605.02688.pdf
  10. Collobert R., Kavukcuoglu K., & Farabet C. (2011). Torch7: A Matlab-like Environment for Machine Learning. http://ronan.collobert.com/pub/matos/2011_torch7_nipsw.pdf
  11. Abadi M., et al. (2015). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. download.tensorflow.org/paper/whitepaper2015.pdf
  12. Abadi, Martín, et al. (2016) TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv:1603.04467. https://arxiv.org/abs/1603.04467
  13. Jouppi, N P, et al. (2017). In-Datacenter Performance Analysis of a Tensor Processing Unit. arXiv:1704.04760. https://arxiv.org/abs/1704.04760
  14. van Merriënboer, B., Bahdanau, D., Dumoulin, V., Serdyuk, D., Warde-Farley, D., Chorowski, J., Bengio, Y. (2015). Blocks and Fuel: Frameworks for deep learning. arXiv:1506.00619. https://arxiv.org/pdf/1506.00619.pdf
  15. https://stackoverflow.com/questions/57273888/keras-vs-TensorFlow-code-comparison-sources
  16. Harris M. (2016). Docker vs. Virtual Machine. Nvidia developer blog. https://developer.nvidia.com/blog/nvidia-docker-gpu-server-application-deployment-made-easy/vm_vs_docker/
  17. A visual play on words — the project's original code name was Seven of Nine, a Borg character from the series Star Trek: Voyager
  18. Kubernetes Components. (2021, March 18) Kubernetes. https://kubernetes.io/docs/concepts/overview/components/
  19. Pavlou C. (2019). An end-to-end ML pipeline on-prem: Notebooks & Kubeflow Pipelines on the new MiniKF. Medium | Kubeflow. https://medium.com/kubeflow/an-end-to-end-ml-pipeline-on-prem-notebooks-kubeflow-pipelines-on-the-new-minikf-33b7d8e9a836
  20. Vargo S. (2017). Managing Google Calendar with Terraform. HashiCorp. https://www.hashicorp.com/blog/managing-google-calendar-with-terraform
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Generative AI with Python and TensorFlow 2
Published in: Apr 2021
Publisher: Packt
ISBN-13: 9781800200883
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