mpy @mpowerpy

Six new multi-year @NASA projects will study everything from retreating glaciers to air quality to clouds generated by wildfires. Despite their different topics, all of the missions will use sensors mounted on aircraft. go.nasa.gov/4bBIsVy

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2nd Edition at amzn.to/45Y3LyI Graph Machine Learning — the latest advancements in graph data to build robust machine learning algorithms. 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🟠Master new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL) 🔵Explore GML frameworks and their main characteristics 🟠Leverage LLMs for machine learning on graphs and learn about temporal learning

Backpropagation Explained for Beginners: Building the Intuition! #BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/BackpropIntuit… geni.us/Backprop-Graph

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Mathematical Foundations of Quantum Computing: A Scaffolding Approach Get it at amzn.to/48bSyus

One day left to register for our webinar 'Satellite retrievals for wildfires & related applications'. Find out how satellite data from the @esa Climate Change Initiative Fire project is used to track global burned area and fire emissions and why accurate wildfire information matters for the climate system, civil protection, and environmental policy. This webinar is ideal for fire scientists, climate modellers, and environmental stakeholders.🔥🛰️ Register here: t1p.de/xuyrj

🔥 Wildfires across central Canada 🇨🇦 sent vast smoke plumes across eastern Canada, the US and the Atlantic in mid-July 2026. 🛰️ This #CopernicusEU Sentinel-5P time series tracks the smoke's eastward transport, supporting air quality monitoring. 🔗 link.europa.eu/fCpVRK

8 RAG architectures for AI Engineers: (explained with usage) 1) Naive RAG - Retrieves documents purely based on vector similarity between the query embedding and stored embeddings. - Works best for simple, fact-based queries where direct semantic matching suffices. 2) Multimodal RAG - Handles multiple data types (text, images, audio, etc.) by embedding and retrieving across modalities. - Ideal for cross-modal retrieval tasks like answering a text query with both text and image context. 3) HyDE (Hypothetical Document Embeddings) - Queries are not semantically similar to documents. - This technique generates a hypothetical answer document from the query before retrieval. - Uses this generated document’s embedding to find more relevant real documents. 4) Corrective RAG - Validates retrieved results by comparing them against trusted sources (e.g., web search). - Ensures up-to-date and accurate information, filtering or correcting retrieved content before passing to the LLM. 5) Graph RAG - Converts retrieved content into a knowledge graph to capture relationships and entities. - Enhances reasoning by providing structured context alongside raw text to the LLM. 6) Hybrid RAG - Combines dense vector retrieval with graph-based retrieval in a single pipeline. - Useful when the task requires both unstructured text and structured relational data for richer answers. 7) Adaptive RAG - Dynamically decides if a query requires a simple direct retrieval or a multi-step reasoning chain. - Breaks complex queries into smaller sub-queries for better coverage and accuracy. 8) Agentic RAG - Uses AI agents with planning, reasoning (ReAct, CoT), and memory to orchestrate retrieval from multiple sources. - Best suited for complex workflows that require tool use, external APIs, or combining multiple RAG techniques. Most architectures here involve some form of retrieval-time decision. But they all run on top of whatever was already indexed. If that indexing step outputs messy chunks, every architecture inherits them. Improving it is a separate problem from the 8 above. I wrote about a better unit for the indexing step. The technique: - cuts corpus size by 40x. - reduces tokens per query by 3x. - improves vector search relevance by 2.3x. And it doesn't alter the retrieval algorithm, the reranker, or the embedding model. Read it in the article quoted below.

Akshay 🚀 @akshay_pachaar

3 months ago

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13 83 708 1.4M 2K

Hands-On Graph Neural Networks Using Python: Build, Train, and Optimize Graph-Based Deep Learning Models with PyTorch Geometric and Python! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #Books #100DaysofCode geni.us/Graph-NN--Pyth…

there are four types of agent loops. most people only know one. loop engineering is a choice between four structures, each handing off one more job than the last. every one answers two questions: what starts a run, and what ends it. hand-run, you answer both yourself, every time. 1) turn-based → you prompt, it acts, you review, you prompt again. both jobs stay with you. use when requirements are still forming. 2) goal-based → "/goal hit Lighthouse 90, stop after 5 tries." an evaluator checks, a no sends it back. use when the outcome is measurable but the path isn't. 3) time-based → a clock fires, it runs "check the PR, fix CI," then waits. /loop local, /schedule survives a closed laptop. use for recurring work. 4) proactive → no human present. it watches a channel, spawns triage, fix, and a reviewer, closes the task itself. use for standing duties you can't predict. not which one is most advanced. whether your task is exploratory, measurable, recurring, or standing. the more you hand off, the less you babysit. full breakdown in the article below.

Hanako @hanakoxbt

2 weeks ago

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19 74 438 1.3M 1K

"Digital Twins in Action", by Greg Biegel Pre-order now with Amazon price guarantee: amzn.to/4brDn1U from @ManningBooks You will learn how to: • Define clear business objectives for digital twins • Create digital representations of physical systems • Blend computer vision, OCR, and generative AI with 3D geometric models • Stream IoT sensor data into a twin • Represent real-world systems as knowledge graphs • Machine learning and agentic AI for analysis and decision-making

Build your foundation in #quantumcomputing: explore basic logic, quantum circuits, entanglement and algorithms #WithWolfram

🌟 🌟 🌟 🌟 🌟 Amazing 5-star book: “Dancing with #Python — Learn to code with Python **and** Quantum Computing” at amzn.to/3ERsiEC (744 pages)

This study examines wave behavior near extremal black hole horizons, using the Couch–Torrence inversion to clarify the deep analogy between horizon peeling and behavior near infinity in black hole spacetimes. 🔗worldscientific.com/doi/reader/10.…

"How to Build and Fine‐Tune a Small Language Model: A Step-by-Step Guide for Beginners, Researchers, and Non-Programmers" Available at amzn.to/4aV0amA

Learn Quantum Computing with Python and IBM Quantum — Write your own practical quantum programs [2nd Edition]: amzn.to/3F3PdlD v/ @PacktDataML 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: 🟠Get familiar with the features within the IBM Quantum Platform 🔴Create and visualize quantum gates and circuits 🟠Operate quantum gates on qubits using the IBM Quantum Composer 🔴Install and run your quantum circuits on an IBM Quantum computer 🟠Discover Qiskit and its many features such as the Qiskit Runtime 🔴Get to grips with fundamental quantum algorithms and error mitigation techniques to help you get started 🟠Understand the new era of quantum utility and how this moves us closer towards quantum advantage

🌊🌊 #Ocean Colour Estimates of #Phytoplankton Diversity in the #Mediterranean Sea: Update of the Operational Regional Algorithms Within the #Copernicus #Marine Service ✍️ Annalisa Di Cicco et al. 🔗 brnw.ch/21x3Hre

أحد المراجع المهمة التي تمكنك من كتابة بحث علمي على أسس علمية. shuyiwrites.com/uploads/1/3/0/…

5 Powerful lessons from " Fall in love with the problem not the solution"

Statistics Every Programmer Needs — Practical Python implementations and quantitative methods: amzn.to/4vwf1fJ via @ManningBooks 🌟🌟🌟🌟🌟 This hands-on 448-page guide teaches you how to: • Apply foundational and advanced statistical techniques • Build predictive models and simulations • Optimize decisions under constraints • Interpret and validate results with statistical rigor • Implement quantitative methods using Python

Complex Network Analysis in Python — Recognize • Construct • Visualize • Analyze • Interpret: amzn.to/2Rw8ZKL —————— #DataScientist #DataScience #AI #ML #MachineLearning