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Generative AI with Python and PyTorch

Generative AI with Python and PyTorch - Second Edition

By : Joseph Babcock, Raghav Bali
5 (1)
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Generative AI with Python and PyTorch

Generative AI with Python and PyTorch

5 (1)
By: Joseph Babcock, Raghav Bali

Overview of this book

Become an expert in Generative AI through hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. This book is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable. From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence. You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models. Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI. *Email sign-up and proof of purchase required
Table of Contents (19 chapters)
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17
Other Books You May Enjoy
18
Index

References

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  9. Deepfake Image Sample. Wikimedia. https://upload.wikimedia.org/wikipedia/en/thumb/7/71/Deepfake_example.gif/280px-Deepfake_example.gif.
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  17. Mitchell, Tom M. 2015. “Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression.” Machine Learning.
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  19. Ho, Tin Kam. 1995. “Random Decision Forests.” Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, August 14–16, 1995, 278–282.
  20. Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32.
  21. Friedman, J. H. 1999. “Greedy Function Approximation: A Gradient Boosting Machine.”
  22. Cortes, Corinna, and Vladimir N. Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20 (3): 273–297.
  23. Kingma, Diederik P., and Max Welling. 2022. “Auto-Encoding Variational Bayes.” arXiv. https://arxiv.org/abs/1312.6114.
  24. Sample Images from a VAE: https://miro.medium.com/max/2880/1*jcCjbdnN4uEowuHfBoqITQ.jpeg
  25. Chen, Ricky T. Q., Xuechen Li, Roger Grosse, and David Duvenaud. 2019. “Isolating Sources of Disentanglement in VAEs.” arXiv Vanity. https://www.arxiv-vanity.com/papers/1802.04942/.
  26. Esser, Patrick, Johannes Haux, and Björn Ommer. 2019. “Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis.” arXiv. https://arxiv.org/pdf/1910.10223.pdf.
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  28. Bourached, Anthony, and George Cann. 2019. “Raiders of the Lost Art.” arXiv. https://arxiv.org/pdf/1909.05677.pdf.
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  31. Gorti, Satya, and Jeremy Ma. 2018. “Text-to-Image-to-Text Translation Using Cycle Consistent Adversarial Networks.”
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  33. Weizenbaum, Joseph. 1976. Computer Power and Human Reason: From Judgment to Calculation. New York: W. H. Freeman and Company.
  34. Schwartz, Barry. 2019. “Welcome BERT: Google’s Latest Search Algorithm to Better Understand Natural Language.” Search Engine Land. https://searchengineland.com/welcome-bert-google-artificial-intelligence-for-understanding-search-queries-323976.
  35. X post: https://x.com/TonyHoWasHere/status/1636347961813655557.
  36. TheSequence. 2023. “Edge 314: A Deep Dive into Llama 2: Meta AI LLM That Has Become a Symbol in Open Source AI.” https://thesequence.substack.com/p/a-deep-dive-into-llama-2-meta-ai.
  37. Gupta, Anant, Srivas Venkatesh, Sumit Chopra, and Christian Ledig. 2019. “Generative Image Translation for Data Augmentation of Bone Lesion Pathology.” Proceedings of Machine Learning Research. https://proceedings.mlr.press/v102/gupta19b.html.
  38. Mulé, Sébastien, Littisha Lawrance, Younes Belkouchi, and Valérie Vilgrain. 2022. “Generative Adversarial Networks (GAN)-Based Data Augmentation of Rare Liver Cancers: The SFR 2021 Artificial Intelligence Data Challenge.” ScienceDirect. https://www.sciencedirect.com/science/article/pii/S2211568422001711.
  39. Shapiro, Danny. 2023. “Generative AI Revs Up New Age in Auto Industry, from Design and Engineering to Production and Sales.” NVIDIA Blog. https://blogs.nvidia.com/blog/generative-ai-auto-industry/.

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