#MachineLearning

"Mathematical Pathways to Machine Learning" is a new introduction to the mathematical foundations of machine learning, published in August 2026. It is a collection of lecture notes that develops the mathematics from first principles and covers vectors and matrices, inner and outer products, orthogonality and projections, Gram–Schmidt and QR factorization, eigenvalues and eigenvectors, SVD, PCA, derivatives and gradients, Jacobians, Hessians, Taylor approximations, convexity, least squares, gradient descent, convergence and conditioning, Newton's method, momentum, Lagrange multipliers, KKT conditions, duality, and support vector machines. The mathematical concepts are directly connected to actual machine learning applications, including neural networks and backpropagation, PageRank, recommender systems, dimensionality reduction, regression, optimization, and SVMs. The presentation is accessible and very well structured, with worked examples and exercises throughout, including exam-practice problems and small computational exercises.

"Machine Learning: The Basics" by Alexander Jung A great, compact refresher on the core concepts of machine learning through the data–model–loss framework. It explains: - hypothesis spaces - model selection - empirical risk minimization and regularization - probabilistic models and clustering - federated learning - privacy and explainability

Project Suncatcher has liftoff! ☀️🚀 Announced last year, this moonshot project explores if we can one day host machine learning infrastructure in space. After years of research, we’ve just launched a prototype satellite, built in partnership with @planet, into orbit on @SpaceX's Transporter-18 rideshare mission. This initial mission will gather data on how @Google TPUs handle the physical stress and extremes of spaceflight. Ultimately, these findings will help us explore how to potentially link multiple satellite constellations for scaled machine learning. By operating in low Earth orbit, these systems can tap into near-constant sunlight to generate up to 8x more solar power than on Earth.

This is free f*cking gold for AI engineers.. 10 repos worth keeping, from Python basics all the way to LLMs, agents and production AI. Forget the random tutorials. 1. Python-100-Days -- 100 days through fundamentals, data analysis and web dev. ↳ 2. Generative AI for Beginners -- LLM basics, prompting, RAG, agents and fine-tuning. ↳ 3. LLMs from Scratch -- build one yourself: tokenization, attention, transformers, training. ↳ 4. ML for Beginners -- 26 lessons of classical ML across 12 weeks. ↳ 5. OpenAI Cookbook -- working examples that take you from reading to building. ↳ 6. Stable Diffusion -- the original implementation and the research code behind it. ↳ 7. AI Agents for Beginners -- tool use, RAG, agent frameworks and multi-agent systems. ↳ 8. AI for Beginners -- 24 lessons over 12 weeks: neural nets, vision, NLP. ↳ 9. LLM App -- RAG pipelines, enterprise search, real-time data and vector search. ↳ 10. Segment Anything -- Meta's foundation model for image segmentation. ↳ Start from wherever you are: > Python -> Python-100-Days. > ML -> ML-For-Beginners. > LLMs -> LLMs-from-scratch. > Agents -> AI-Agents-for-Beginners. > Building -> OpenAI Cookbook, LLM App. > Vision -> Segment Anything. Pick one, build something real with it, then move on to the next.

THE AI/ML ENGINEER GITHUB GOLDMINE 📌 These repositories cover everything from ML fundamentals and mathematics to neural networks, LLMs, RAG, AI agents, and reinforcement learning. Save this list. You will keep coming back to it. 1. Machine Learning for Beginners Microsoft A structured introduction to classical ML using Python and Scikit-learn. 2. 100 Days of Machine Learning Code A daily learning approach covering practical ML concepts, notebooks and implementations. 3. The Algorithms in Python Algorithms implemented in Python for learning. Great for strengthening the algorithmic and mathematical thinking that helps with ML engineering. 4. Mathematics for Machine Learning A collection covering: Linear Algebra Calculus Probability Optimization Dimensionality Reduction Perfect if ML mathematics feels like a weak point. 5. Made With ML One of the best resources for understanding how ML moves beyond notebooks. Covers: ML foundations Modeling Deep Learning Data quality MLOps Production ML 6. Annotated Deep Learning Paper Implementations 60+ deep learning papers implemented with detailed explanations. Includes: Transformers Attention GANs Optimizers Vision Transformers Distillation Reinforcement Learning 7. Neural Networks: Zero to Hero Andrej Karpathy's famous course. Build neural networks from scratch and understand what is happening under the hood. Includes: Backpropagation Neural networks Tokenization GPT Transformers 8. Hands-On Large Language Models The code repository for the "Hands-On Large Language Models" book. Covers practical LLM concepts through notebooks and implementations. 9. Prompt Engineering Guide Not just prompts. This repository covers: Prompt engineering Context engineering RAG AI Agents Papers Notebooks Datasets 10. AI Agents for Beginners Microsoft's hands-on course for learning how to build AI agents. 12 lessons covering the fundamentals of agentic AI with code examples. 11. RAG Techniques A serious resource if you want to work on production LLM applications. Includes techniques such as: Basic RAG Chunking Query transformation HyDE Reranking Fusion Retrieval Graph RAG RAPTOR Self-RAG Corrective RAG Agentic RAG RAG Evaluation 12. Real-World Data Science Projects Hands-on projects covering areas such as: Forecasting Classification Recommendation Systems Anomaly Detection NLP Computer Vision MLOps 13. Awesome NLP A massive curated collection of NLP resources. Includes: Libraries Datasets Research Papers Tutorials Courses Books NLP techniques 14. Awesome Reinforcement Learning A curated collection of RL resources covering research, tutorials, libraries and learning material. 15. Reinforcement Learning Algorithms Implement reinforcement learning algorithms from scratch. Useful for understanding what happens underneath RL libraries. 16. RL Algorithms From Scratch A collection of reinforcement learning algorithms implemented from scratch in Python. Includes algorithms such as: Q-Learning SARSA DQN REINFORCE Actor-Critic PPO DDPG SAC TRPO THE AI/ML ENGINEER LEARNING STACK ML Fundamentals ↓ Mathematics ↓ Neural Networks ↓ Deep Learning ↓ Transformers ↓ LLMs ↓ RAG ↓ AI Agents ↓ MLOps ↓ Production AI DON'T JUST STAR THESE REPOS.

Small-Molecule 1H and 13C NMR: From Chemical-Shift Prediction to Automated Structure Elucidation #machinelearning #compchem

🚀 30 AI Algorithms Every Data Scientist Should Know From Machine Learning to Deep Learning, these algorithms are essential for understanding how AI works. Save this cheat sheet for future reference! #AI #MachineLearning #DataScience

Machine Learning from Scratch 🚀 A 33K-star GitHub repo rebuilds machine learning algorithms from scratch, from linear regression to GANs and deep reinforcement learning. It's called ML-From-Scratch, created by Erik Linder-Noren, and the entire project is designed around understanding the implementations rather than optimizing them for speed. The repository contains readable Python implementations of the algorithms normally hidden behind high-level machine learning libraries. It covers - → Linear and logistic regression → Decision trees, random forests and gradient boosting → XGBoost and support vector machines → K-means, DBSCAN, PCA and Gaussian mixtures → Neural networks, CNNs and RNNs → Generative adversarial networks → Deep Q-learning for CartPole The neural-network code is especially useful. It includes implementations of convolutional layers, pooling, batch normalization, dropout, activation functions, loss functions and optimizers. You can follow how the components fit together to train a convolutional classifier, generate handwritten digits with a GAN, or train a reinforcement-learning agent. Most of the numerical work is built around NumPy, with additional scientific Python dependencies where needed. The implementations prioritize readability over performance. It's an older codebase, so some examples need dependency updates to run with current libraries. For anyone who has studied machine learning theory and wants to examine the actual algorithms, this is an excellent reference to work through. GitHub:

Since it's summer, and more or less internship and tech interview season, I made all 30 chapters of my Machine Learning Q and AI book freely available for the summer: Hope it’s helpful! Happy reading, and good luck if you are interviewing!

NumPy. Pandas. PyTorch. Most AI starts as a Python script. The heavy math runs in C++ and CUDA, but Python is the glue holding it together. AI is the product. Python is how it's built. #Python #AI #MachineLearning #BackendEngineering

Machine Learning Methods — Free 874-Page Open Access Textbook! Pre-Order, Now! Hang Li’s Springer 2026 book, a follow up 2nd Edition is an enormous reference spanning the four core branches of Machine Learning: • Supervised Learning • Unsupervised Learning • Deep Learning • Reinforcement Learning • 80+ essential algorithms • GBDT + EM Algorithm • Transformers • Diffusion Models • PPO • Mathematical formulations + intuitive explanation • Practical examples+ Machine Learning real-world relevance At 874 pages with 260 illustrations, it is designed for advanced undergraduate/graduate students of ML, educators and AI professionals. Springer explicitly marks the textbook Open Access, free and unlimited access. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Books #Coding #100DaysofCode Free Open Access: Amazon:

📂 Cheat sheets for data science and machine learning: #DataScience #MachineLearning #CheatSheet

#ArtificialIntelligence has discovered the future of pitching in #baseball. #MachineLearning

Overheard: MLE, as Machine Learning Engineers(not researchers) will be the next job being replaced by AI, faster than senior engineers. Turns out, AI is very good at testing parameters to tune and optimize the shit out of it, given a clear optimization goal. Agree?

Redwood Research proposes 'opaque serial depth' as a quantifiable proxy for unverbalized serial cognition in AI models, addressing future… #MachineLearning #AI #LLM #DeepLearning #AgenticAI

Learning Machine Learning in the era of Al should not be overwhelming, In this video I shared the 3 free resources you need to be a good machine learning engineer. Comment "ML" for these all resources. #ai #softwaredeveloper #machinelearning #maths #tech

Implementing Agentic RAG on #Azure using code #MachineLearning #ChatGPT

Introduction to Machine Learning Systems 📕 Get Now: #MachineLearning 🤖 | #MLSystems ⚙️ | #ArtificialIntelligence 🧠 | #MLOps 🚀 | #DataScience 📊

For sale: - Ideal for photonics, AI imaging, or LLM interpretability startup. #Photonics #AI #MachineLearning #DeepTech #ComputerVision #Optics #AIResearch

#Azure AI Guided Technical Labs #MachineLearning #ChatGPT