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    Generative AI with LangChain. Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

    (ebook) (audiobook) (audiobook) Język publikacji: angielski
    Generative AI with LangChain. Build large language model (LLM) apps with Python, ChatGPT, and other LLMs Ben Auffarth - okładka ebooka

    Generative AI with LangChain. Build large language model (LLM) apps with Python, ChatGPT, and other LLMs Ben Auffarth - okładka ebooka

    Generative AI with LangChain. Build large language model (LLM) apps with Python, ChatGPT, and other LLMs Ben Auffarth - okładka audiobooka MP3

    Generative AI with LangChain. Build large language model (LLM) apps with Python, ChatGPT, and other LLMs Ben Auffarth - okładka audiobooks CD

    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    360
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    ePub

    Ebook

    139,00 zł

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    ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications.

    Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.

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    O autorze ebooka

    Ben Auffarth is a full-stack data scientist who has >15 years of work experience. With a background and Ph.D. in computational and cognitive neuroscience from one of Europe's top engineering universities, he has designed and conducted wet lab experiments on cell cultures, analyzed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores, built production systems processing hundreds of thousands of transactions per day, and trained neural networks on millions of text documents. In his work, he often notices a lack of appreciation for the importance of time-related factors, a deficit he wanted to address in this book. He co-founded and is the former president of Data Science Speakers, London.

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