“Generative AI with Python: The Developer’s Guide to Pretrained LLMs, Vector Databases, Retrieval Augmented Generation, and Agentic Systems” Available at http://
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SOFTWARE
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Generative AI with Python: Developer’s Guide to LLMs, Vector DBs, RAG, Agents
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Agentic Architectural Patterns for Building Multi-Agent Systems
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5- release from @PacktDataML at http://
amzn.to/3MaHy8T "Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems" Contents:
GenAI in the Enterprise: Landscape, -
Machine Learning Engineering on AWS: Build AI Agents
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"Machine Learning Engineering on AWS: Build, deploy, and operationalize LLMs, AI agents, and generative AI systems on AWS" — https://
amzn.to/4od7C1I v/ @PacktDataML 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂 𝗪𝗶𝗹𝗹 𝗟𝗲𝗮𝗿𝗻:
Build and deploy AI agents using Bedrock AgentCore and Strands Agents -
Graph Machine Learning (2nd Edition): New PyTorch Geometric examples
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Graph Machine Learning — Latest advancements in Graph Data to build robust Machine Learning algorithms (2nd Edition) — at http://
amzn.to/45Y3LyI v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
Master new graph ML techniques through updated examples using PyTorch Geometric and Deep -
50 ML Projects to Understand LLMs via Data Analysis and Visualization
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50 ML projects to understand LLMs — Investigate transformer mechanisms through data analysis, visualization, and experimentation: http://
amzn.to/3P8ztDt via @PacktPublishing @PacktDataML —————
#AI #GenAI #MachineLearning #DataScientist #DataScience -
Book on building AI-enhanced web apps with LLMs
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Build AI-Enhanced Web Apps: http://
amzn.to/4bgux6d
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Amazon Summary: "This book shows you step-by-step and example-by-example how to build sites and applications that take advantage of large language models (LLMs) like GPT, Claude, and Llama. Written especially for web -
Building AI Agents with LLMs, RAG, and Knowledge Graphs
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"Building AI Agents with LLMs, RAG, and Knowledge Graphs — A practical guide to autonomous and modern AI agents" See it at http://
amzn.to/4622k2h via @PacktDataML -
Practical Guide to Building LLM and RAG Apps with LangChain and Python
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"Generative AI and RAG for Beginners: A Practical Step-by-Step Guide to Building LLM and RAG Applications with LangChain and Python" Get your copy at http://
amzn.to/3MZZ9R5 Independently published: December 2025
Print length: 255 pages -
Book: Build a Large Language Model from Scratch by Raschka
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<<💡Best Seller🚀>>
— Kirk Borne (@KirkDBorne) 15 juin 2026
Build a Large Language Model (From Scratch): https://t.co/vvahfx1VTv by @rasbt v/ @ManningBooks
—————#DataScience #DataScientist #MachineLearning #ML #DeepLearning #LLMs #AI #GenAI https://t.co/MbmiX2Olme pic.twitter.com/7My9FOhSJl<>
Build a Large Language Model (From Scratch): https://
amzn.to/3WYFNO5 by @rasbt v/ @ManningBooks —————
#DataScience #DataScientist #MachineLearning #ML #DeepLearning #LLMs #AI #GenAI -
Moonshot AI’s Kimi-K2.7-Code: open-source coding agent with 256K context
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Moonshot AI just flipped cost vs. capability for coding agents – follow us for more AI tools updates, visit http://
futurepedia.io your leading AI tools directory. Moonshot’s Kimi‑K2.7‑Code is live and open‑source: an agentic MoE (~1T, 32B active) with 256K context + vision,