Transformers for Natural Language Processing and Computer Vision - Third Edition: Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3
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Summary
Description
Unleash the full potential of transformers with this comprehensive guide covering architecture, capabilities, risks, and practical implementations on OpenAI, Google Vertex AI, and Hugging FacePurchase of the print or Kindle book includes a free eBook in PDF format Key Features
- Master NLP and vision transformers, from the architecture to fine-tuning and implementation
- Learn how to apply Retrieval Augmented Generation (RAG) with LLMs using customized texts and embeddings
- Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases
- Learn how to pretrain and fine-tune LLMs
- Learn how to work with multiple platforms, such as Hugging Face, OpenAI, and Google Vertex AI
- Learn about different tokenizers and the best practices for preprocessing language data
- Implement Retrieval Augmented Generation and rules bases to mitigate hallucinations
- Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP
- Create and implement cross-platform chained models, such as HuggingGPT
- Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V
This book is ideal for NLP and CV engineers, software developers, data scientists, machine learning engineers, and technical leaders looking to advance their LLMs and generative AI skills or explore the latest trends in the field.Knowledge of Python and machine learning concepts is required to fully understand the use cases and code examples. However, with examples using LLM user interfaces, prompt engineering, and no-code model building, this book is great for anyone curious about the AI revolution.
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