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
AI
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LLM Engineer’s Handbook: from concept to production guide
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The Agentic AI Playbook 2026 turns LLMs into reliable AI agents
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The Agentic AI Playbook 2026 Edition Turns LLMs into Reliable AI Agents: http://
amzn.to/49zjzId -
New book: A Practical Guide to Reinforcement Learning from Human Feedback
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New release from @PacktDataML at http://
amzn.to/3PMn1ZL "A Practical Guide to Reinforcement Learning from Human Feedback (RLHF)" 𝗔𝗺𝗮𝘇𝗼𝗻 𝘀𝘂𝗺𝗺𝗮𝗿𝘆: RLHF is a powerful approach to AI alignment and human-centered machine learning. By combining reinforcement learning -
Mastering PyTorch: Create and deploy CNN to multimodal models and LLMs
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"Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond" – http://
amzn.to/40IFEQR via @PacktDataML —————
#AI #ML #MachineLearning #DataScience #DataScientist #GenAI -
Super Study Guide for Transformers and Large Language Models
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Super Study Guide: Transformers & Large Language Models: http://
amzn.to/3SW6YYm by @afshinea & @shervinea Beautifully presented, excellent content, timely, thorough, educational #LLMs #MachineLearning #AI #GenAI #DataScience #DataScientist #GenerativeAI -
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 -
Self-Evolving Multi-Agent Systems via Decentralized Memory
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Multi-agent systems typically share a single memory pool, but this causes agents to converge towards the same behavior and leads to a loss of useful specialization. This article attributes to
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Dense Supervision, Sparse Updates in On-Policy Distillation
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"Dense Supervision, Sparse Updates: On the Sparsity and Geometry of On-Policy Distillation" OPD uses dense teacher feedback on student-generated rollouts, so we might expect dense parameter rewriting. However, this paper shows that is not the case. They found that OPD updates
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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 -
Recent Open Source LLM Rankings: Kimi, GLM, MiniMax
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Recent Opensource LLMs releases rankings for me 1st place: Kimi K2.7 Coding 2nd place: GLM 5.2 3rd place: MiniMax M3