Transformers for Natural Language Processing and Computer Vision: Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3

"Transformers for Natural Language Processing and Computer Vision, Third  Edition" by Denis Rothman explores the architectures, applications, and  platforms used for Natural Language Processing (NLP) and Computer  Vision (CV). This comprehensive guide covers various transformer  architectures, from their introduction to the latest developments in  Foundation Models and Generative AI. The book instructs readers on  pretraining and fine-tuning Large Language Models (LLMs), applying them  to use cases ranging from summarization to question-answering systems,  and navigating the potential risks associated with these models. It also  delves into generative vision transformers, multimodal model  architectures, and combines different models and platforms to enhance  learning about AI agent replication. Additionally, the book addresses  the importance of moderating models with rule and knowledge bases to  mitigate risks such as hallucinations, memorization, and privacy  concerns. Finally, it provides insights into leveraging Retrieval  Augmented Generation (RAG) with LLMs to enhance model accuracy and  control​

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The book discusses various advanced topics, including the emergence and implications of generative AI revolutionized by models like ChatGPT, the process of fine-tuning GPT models, and the use of interpretable tools for understanding transformer mechanisms. Additionally, it touches upon the significance of tokenizers in transformer models, leveraging LLM embeddings for tasks without extensive fine-tuning, and the application of transformers in cutting-edge fields like semantic role labeling and text summarization. The exploration extends to cutting-edge LLMs with platforms like Vertex AI and PaLM 2, strategies for mitigating risks in LLMs, and the evolution of vision transformers impacting revolutionary AI paradigms. Furthermore, it investigates areas such as transcending the image-text boundary with Stable Diffusion, training vision models with Hugging Face AutoTrain without coding, moving towards functional AGI with HuggingGPT, and innovating beyond human-designed prompts through generative ideation​.