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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 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 (18 chapters)
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16
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Index

Instruction fine-tuning

Instruction fine-tuning is similar to supervised fine-tuning (SFT), where the dataset consists of input-output pairs specific to the task. However, the key difference is that in instruction fine-tuning, the input for each data point includes not just the context but also an explicit task instruction, while the model is trained using the same language modeling objective. This contrasts with SFT, where the dataset consists of input-output pairs, and the training objective is tailored to the specific task (e.g., using cross-entropy for training a classifier). Instruction tuning helps the model generalize and align better with tasks while retaining its language modeling capabilities. Figure 5.3 contrasts examples of SFT and instruction tuning.

Figure 5.3: Comparing the dataset setup between supervised fine-tuning and instruction tuning

Figure 5.3: Comparing the dataset setup between supervised fine-tuning and instruction tuning

The authors of the InstructGPT paper demonstrated that incorporating instructions enables the model to better understand...

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