New release from @PacktDataML available at: http://
amzn.to/40Sp4O9 "Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based Agentic AI Framework with tool use, memory, and multi-agent workflows" Table of Contents:
Introduction to Generative AI and AI
LLMS
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New Technical Guide to Designing Multi-Agent AI Systems with MCP
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LLM Engineer’s Handbook for Model Development and Training
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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 -
Guide to Building Production-Ready AI Agent Systems
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30 Agents Every AI Engineer Must Build — Build production-ready agent systems using proven architectures and patterns: http://
amzn.to/41ckg6z v/ @PacktDataML —
What you will learn:
Deploy production-ready agent systems that scale securely and reliably
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Enhancing Generative AI Systems with RAG Integration
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Unlocking Data with #GenerativeAI and RAG — Enhance Generative AI systems by integrating internal data with large language models using RAG: http://
amzn.to/3PtFHdv v/ @PacktDataML -
Practical Guide to Building AI Agents with LLMs and RAG
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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 -
δ-mem: Efficient Online Memory for Large Language Models
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“δ-mem: Efficient Online Memory for Large Language Models” LLMs need long-term memory, but extending context is expensive and often doesn’t mean the model actually uses the history well. What this paper did is to store past information in a tiny 8×8 associative memory state,
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Efficient LLM Pre-Training Using Token Superposition
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“Efficient Pre-Training with Token Superposition” LLM pretraining is bottlenecked by how many useful tokens you can consume per FLOP. This paper temporarily merge nearby tokens into averaged embedding bags, train the model to predict the next bag, then switch back to normal
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Building an AI-Powered File Reader using RAG and LLMs
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RAG with LLM: Creating an AI-Powered File Reader! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding
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Announcing Agentic Generative AI for Autonomous LLM Systems
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Announcing Agentic Generative AI to Build Autonomous AI Systems with LLM! @SantaClaraUniv #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless
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Advancement of Autonomous AI Cyber Capabilities at the Frontier
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How Fast is Autonomous AI Cyber Capability Advancing at the Frontier? #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux