Take your AI to the Edge — Solve Real-World Problems with Embedded Machine Learning : https://
amzn.to/3GN70uC by @dansitu and @jennymplunkett —————
#IoT #IIoT #AIoT #EdgeAI #ML #DataScience #EdgeComputing #DataScientist
MACHINE LEARNING
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Embedded Machine Learning: Solving Real-World Problems at Edge
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Context Engineering for Multi-Agent Systems: Building Transparent Reasoning Architectures
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"Context Engineering for Multi-Agent Systems: Move beyond prompting to build a Context Engine, a transparent architecture of context and reasoning" — at http://
amzn.to/448dSiA v/ @PacktDataML 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷:
Develop memory models to retain short-term and -
Practical Guide to Reinforcement Learning from Human Feedback
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New release from @PacktDataML available 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 -
Graph Machine Learning Advancements with PyTorch Geometric Techniques
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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 -
Multi-Agent AI Systems Design Using MCP Framework
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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 -
AI’s Role in Navigating Complex Biological Systems and Cancer Neuroscience
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An obvious way to release Mythos class models with uncertain autonomous ability is to make them only available on the website, like Gemini Deep Think or ChatGPT Pro. Minimal risk of being used for autonomous hacking, but accessible to people who have hard problems to solve.
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From Toys to Tools of War: Technology Evolution
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From Toys to Tools of War: How Necessity Fuels Relentless Technological Evolution
— Ronald van Loon (@Ronald_vanLoon) 19 avril 2026
by @_fluxfeeds
#Robotics #MachineLearning #ArtificialIntelligence #ML #Innovation pic.twitter.com/bc9J8o5pB6From Toys to Tools of War: How Necessity Fuels Relentless Technological Evolution
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AutoSOTA: AI agents automatically improve research papers
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What if an AI agent could read a research paper and automatically build an even better version of it? Researchers from Tsinghua University and collaborating institutions just released AutoSOTA to do exactly that. AutoSOTA is a multi-agent system that acts like a fully automated
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Multimodal LLMs vs YOLO: Why Specialized Tools Win
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Why Multimodal LLMs Are the Wrong Tool for Object Detection
— Satya Mallick (@LearnOpenCV) 19 avril 2026
Opus 4.7 vs GPT 5.4 vs YOLO — I tested multimodal LLMs on a simple car detection task. The results? Minutes of processing, missed objects, and bad localization. A purpose-built detector like YOLO does it in milliseconds… pic.twitter.com/vRJrANgtq2Why Multimodal LLMs Are the Wrong Tool for Object Detection Opus 4.7 vs GPT 5.4 vs YOLO — I tested multimodal LLMs on a simple car detection task. The results? Minutes of processing, missed objects, and bad localization. A purpose-built detector like YOLO does it in milliseconds
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Position Encoding Evolution: Sinusoidal to RoPE to YaRN
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Position Encoding Transformers
— Satya Mallick (@LearnOpenCV) 19 avril 2026
LLMs don't read words in order — they see everything at once.
Without position encoding, "the cat sat on the mat" and "the mat sat on the cat" are mathematically identical.
Full breakdown: sinusoidal → learned absolute → RoPE → YaRN →… pic.twitter.com/kyByXaJ1J1Position Encoding Transformers
LLMs don't read words in order — they see everything at once.
Without position encoding, "the cat sat on the mat" and "the mat sat on the cat" are mathematically identical.
Full breakdown: sinusoidal → learned absolute → RoPE → YaRN →