LLM Engineer's Handbook — Master the art of engineering Large Language Models #LLMs from concept to production: http://
amzn.to/4dUQrv6 v/ @PacktDataML Implement robust data pipelines and manage LLM training cycles Create your own LLM and refine with the help of hands-on
@kirkdborne
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LLM Engineer’s Handbook for Training and Production
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DeepSeek in Practice: Comprehensive Guide to LLM Development
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From @PacktDataML at http://
amzn.to/4imbryH "DeepSeek in Practice: From basics to fine-tuning, distillation, agent design, and prompt engineering of open source LLM" Discover DeepSeek's unique traits in the LLM landscape
Compare DeepSeek's multimodal features with leading -
New Book Release: AI-Native LLM Security and Trustworthy AI
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New Release from @PacktDataML — available at http://
amzn.to/4sDpgxM "AI-Native LLM Security — Threats, Defenses, and Best Practices for Building Safe and Trustworthy AI" 𝘽𝙤𝙤𝙠 𝘿𝙚𝙨𝙘𝙧𝙞𝙥𝙩𝙞𝙤𝙣:
"Adversarial AI attacks present a unique set of security challenges, -
Supercharged Coding with Generative AI Tools
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Supercharged Coding with #GenAI — From vibe coding to best practices using GitHub Copilot, ChatGPT, and OpenAI: http://
amzn.to/3VBH2Su v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: Discover how GitHub Copilot, ChatGPT, and the OpenAI API can boost your coding productivity Push -
Architecting AI Software Systems: A Guide to Scalable Integration
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"Architecting AI Software Systems: Crafting robust and scalable AI systems for modern software development" at http://
amzn.to/4oMi9Ag v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
Learn to integrate AI with traditional software architectures, enabling architects to design -
New Book on Generative AI, RAG, and AI Agents Released
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New updated 2nd Edition, by @keithbourne v/ @PacktDataML "Unlocking Data with Generative AI and RAG — Learn AI Agent Fundamentals with RAG-powered Memory, Graph-based RAG, and Intelligent Recall" Get the book here: http://
amzn.to/49zsIkb -
New Edition of Graph Machine Learning Book Released
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2nd Edition — now at http://
amzn.to/45Y3LyI v/ @PacktDataML Graph Machine Learning — Latest advancements in Graph Data to build robust #MachineLearning algorithms 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
Master new graph ML techniques through updated examples using PyTorch Geometric and -
Resource for Developing and Debugging Machine Learning Models
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Develop and Debug high-performance, low-bias, and explainable #MachineLearning and #DeepLearning Models with #Python : http://
amzn.to/3u2JiIB by @AliMLearning via @PacktDataML —————
#DataScientist #DataScience #AI #XAI #ML #PyTorch -
Guide to Building Custom RAG Pipelines
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RAG-Driven #GenerativeAI — Build custom Retrieval Augmented Generation pipelines: http://
amzn.to/3MWnIek v/ @PacktDataML ——
#AI #MachineLearning #DataScience #GenAI #LLMs
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𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
Implement RAG’s traceable outputs, linking each response to its source -
Implementing Genetic Algorithms for Optimization and Simulation
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Hey coders and computational scientists! If you have never implemented Genetic Algorithms for your simulation, parameter search, and optimization problems & challenges, then you have really missed out. Get this book. I used GAs in my galaxy collisions simulation research as a