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Generative AI with Python and PyTorch

Generative AI with Python and PyTorch - Second Edition

By : Joseph Babcock, Raghav Bali
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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 immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch 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.
Table of Contents (19 chapters)
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17
Other Books You May Enjoy
18
Index

LLMs

The authors of InstructGPT demonstrated how instruction tuning and RLHF can significantly improve the alignment and overall utility of language models after the initial pretraining step. InstructGPT was about 100x smaller than GPT-3, yet it outperformed GPT-3 on multiple evaluation criteria. This was followed by GPT-3.5, more commonly known as ChatGPT, which popularized the term large language models.

Since then, LLMs have evolved into a comprehensive domain, encompassing most NLP tasks that previously required specialized models (as recently as 2021). GPT-3.5 was succeeded by GPT-4, with 1.76 trillion parameters, and GPT-4o and o1 (as of the time of writing), offering larger input/context windows and multi-modal capabilities, including support for audio and image input/output. Other notable proprietary models include Google’s Gemini series, Anthropic’s Claude series, and others, which are typically offered as closed-weight APIs due to proprietary and financial...

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