Machine Learning

RAG Architecture https://t.me/MachineLearning9

NumPy looks like a calculator and behaves like a memory model. 🧮 Most tutorials start with functions. This one starts with the five decisions NumPy made before you opened it: an array is one block of memory, shape is a contract between arrays, a slice is a view you can write through, the loop belongs in C, and the axis tells you which direction collapses. 15 chapters, 25 runnable examples, and every output copied from a real execution on NumPy 2.0.2. 🚀 Arrays, shapes and the machinery under the math: https://enghusseinsheikho.substack.com/p/numpy-the-mental-model?r=3hwnr8&utmcampaign=post&utmmedium=web&showWelcomeOnShare=true

pandas — The Mental Model. A practical, example-driven guide in 18 chapters: how tables and indexes actually work, selection and filtering, groupby, reshaping, joins, time series, text, chaining, performance, and what changed in pandas 3.0. Every example is executed, with its real output shown, and the book closes with a one-page cheat sheet. If the field is short, cut it to: A 31-page practical guide to pandas — 18 chapters, every example executed with real output, from indexes and selection to groupby, performance and pandas 3.0. https://t.me/MachineLearning9

🗂 500+ Cheat Sheets on Machine Learning This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning. Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them. ⛓ You can find it here https://github.com/gurovic/MLCheatSheets #MachineLearning #DeepLearning #Python #DataScience #NeuralNetworks #AI ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

The 25 Best Free Resources for Learning AI in 2026 Learning artificial intelligence in 2026 is both easier and harder than it was a couple of years ago. Easier, because the best courses from Stanford and MIT, books by leading researchers, and hands-on lessons from Hugging Face are all openly available. Harder, because there is now far too much material, and the models and tools change every week. We have collected 25 proven free resources and arranged them by stage: from mathematics to LLMs, agents, and reading the latest papers. Read: https://enghusseinsheikho.substack.com/p/the-25-best-free-resources-for-learning?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

🔖 637 interactive modules on ML and AI On VizLearn, you can visually explore: 🫡 Gradient descent; 🫡 SVM and PCA; 🫡 Bayes' theorem; 🫡 BPE tokenization; 🫡 Query, Key, and Value; 🫡 KV cache; 🫡 Quantization; 🫡 Neural networks and LLMs. Change the input data and immediately see the results of the calculations. ⛓ Link to the tutorial https://vizlearn.in/

🔥 Free IT & Dev Study Kits – 2026 Prep, No Signup If you’re prepping for cloud, dev, or AI roles this year, these free kits cover the gaps most people skip — cert basics, hands-on labs, and mock exams. No signup traps, no hidden fees. 📘 Cert E-Book — covers Cisco & AWS fundamentals, good for first-time cert takers → https://bit.ly/4yrk1Ti 🪜 Online Free Course — structured path, works as a refresher before exams → https://bit.ly/4hUprRP ☁️ Free AI Materials — LLM basics to fine-tuning practice notebooks → https://bit.ly/4yt1iXy 📊 Cloud Study Guide — cheat-sheet style, handy for interview prep → https://bit.ly/4el4Eod 🧠 Free Mock Exam — test yourself before booking the real thing → https://bit.ly/4sbWlkl 📌 Save this or forward it to your dev/study group — good resources are worth sharing. 💪 🌐 Community: https://chat.whatsapp.com/CS7VOvQCLZG5BJPazBGm1v 📲 Personalized help: https://wa.link/xd2fu6 #Python #Cloud #AWS #Cisco #AI #MachineLearning #DevOps #ITCertification #FreeResources #TechCareers

Agentic AI is the future. If you want to become god-level with AI agents, save these 10 GitHub repos: 1 Awesome LLM Apps 🔗 https://github.com/Shubhamsaboo/awesome-llm-apps 2 Generative AI for Beginners 🔗 https://github.com/microsoft/generative-ai-for-beginners 3 LLM Course 🔗 https://github.com/mlabonne/llm-course 4 Prompt Engineering Guide 🔗 https://github.com/dair-ai/Prompt-Engineering-Guide 5 AI Agents for Beginners 🔗 https://github.com/microsoft/ai-agents-for-beginners 6 AI Agents Projects 🔗 https://github.com/ashishpatel26/500-AI-Agents-Projects 7 Hugging Face Agents Course 🔗 https://github.com/huggingface/agents-course 8 Awesome AI Agents 🔗 https://github.com/e2b-dev/awesome-ai-agents 9 GenAI Agents 🔗 https://github.com/NirDiamant/GenAI_Agents 10 Agents Towards Production 🔗 https://github.com/NirDiamant/agents-towards-production Anything else you'd add?

You can literally enter a sentence and see how the Transformer processes it. This interactive tool runs a GPT-like model directly in your browser and allows you to trace the entire process: Tokenization → Embeddings → Q/K/V → Attention Mechanism → MLP → Logits → Probabilities of the next token. You can explore attention maps, intermediate values, and adjust parameters like temperature, top-k, and top-p to see how they affect the generation. If Transformers still seem like a collection of blocks connected by arrows, you should definitely try this. https://poloclub.github.io/transformer-explainer/ https://t.me/DataAnalyticsX

"Matrix Differential Calculus for Machine Learning and Beyond" is a free course of lecture materials from MIT, focusing on the mathematics of derivatives of vectors and matrices. The materials cover gradients, Jacobians, derivatives of matrix functions, differentials, derivatives of matrix products, inverse matrices, and determinants, Kronecker products, optimization, and automatic differentiation. It also explores the application of these methods in machine learning. In my opinion, this is a valuable resource for those who are already familiar with the basics of linear algebra and mathematical analysis and want to understand how differentiation is generalized to vectors and matrices and applied in modern optimization problems. https://ocw.mit.edu/courses/18-s096-matrix-calculus-for-machine-learning-and-beyond-january-iap-2023/mit18_s096iap23_lec_full.pdf https://t.me/MachineLearning9

🔖 A Comprehensive Resource for Machine Learning Practice The platform offers: 🫡 1200+ machine learning tasks; 🫡 Mathematics, from linear algebra to PCA; 🫡 Deep Learning, NLP, and Computer Vision; 🫡 Labs with real datasets; 🫡 Learning paths and interview preparation; 🫡 Projects using GPT, RL, and CUDA. Almost everything can be accessed directly in your browser, and basic tasks are available for free. ⛓ Link to the platform https://www.deep-ml.com/ #MachineLearning #DeepLearning #DataScience #AI #Coding #Tech ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Tracking experiments and versioning ML models with MLflow. 🤖 Machine learning development requires saving hyperparameters, metrics, and artifacts for each run to compare results. The MLflow platform logs key metrics and registers trained models in a central registry. We will install MLflow, run a script to log parameters, and register the model. Let's install the mlflow and scikit-learn libraries to conduct and log a test experiment. 📦 pip install mlflow scikit-learn The dependencies for managing ML experiments have been successfully installed. ✅ Now, let's create a Python script called train.py that will train a simple model, log metrics, and save it to MLflow. 🐍 import mlflow from sklearn.ensemble import RandomForestClassifier mlflow.set_experiment("demo_experiment") with mlflow.start_run(): params = {"n_estimators": 100, "max_depth": 5} mlflow.log_params(params) model = RandomForestClassifier(**params) mlflow.log_metric("accuracy", 0.95) mlflow.sklearn.log_model(model, "rf_model") The training script and metric logging are set up and ready to be executed. 🚀 Let's run the training script to capture the results and parameters in the local MLflow storage. python3 train.py The exper

🔖 Official PyTorch Tutorials This collection includes: 🫡 PyTorch fundamentals and torch.nn; 🫡 Computer Vision and transfer learning; 🫡 NLP and RNNs; 🫡 Reinforcement Learning; 🫡 torch.compile and ONNX; 🫡 Distributed Training, FSDP, and Tensor Parallel; 🫡 Profiling and memory optimization; 🫡 Model serving and Ray. A useful resource for both beginners and those already working with PyTorch in production. ⛓https://docs.pytorch.org/tutorials/

A distinctive bot for subscribing to courses on the most popular educational platforms across multiple fields, most notably programming, artificial intelligence, mechanics, medicine, and other sciences. 🎓🤖 This bot provides a free subscription to any course available within the bot. 🆓 https://t.me/UdemySybot?start=ref_418788114 #FreeCourses #Programming #AI #Education #Udemy #TelegramBots

100 Machine Learning Interview Questions and Answers 🤖 ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

"Understanding Transformers and Attention Mechanisms" - a concise mathematical introduction to the attention mechanism, one of the key ideas in modern language models. This document explains tokenization and embeddings, queries, keys, and values, attention scores and weights, multi-head attention, self-attention, causal attention and masking, cross-attention, and the basic structure of the Transformer architecture. It also presents important techniques that make the attention mechanism in modern LLMs more efficient: KV-caching, grouped query attention (GQA), multi-query attention (MQA), and latent attention. https://arxiv.org/pdf/2604.00965

📚 "Fundamentals of Computer Vision" is a free online book published by MIT Press, providing a broad introduction to computer vision from the perspectives of image processing and machine learning. 🔍 It covers topics such as image formation, training and backpropagation, image filtering and Fourier analysis, CNNs, RNNs, and transformers, generative models, representation learning, 3D geometry, motion estimation, object recognition, models that work with images and text, and much more. 💡 I particularly appreciate that the entire book is available directly in HTML, with a clear and user-friendly layout, and numerous diagrams and visualizations that help to understand the concepts. 🔗 https://visionbook.mit.edu/ #ComputerVision #MachineLearning #AI #TechBooks #MITPress #Education ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Normalization vs Standardization 📊 Why they are not the same. One page: the two formulas side by side, two columns from the same table on incompatible scales, the same 400 values shown raw / min-max / standardized so you can see only the location and scale move while the skew stays, a worked example on five numbers, and a "which one, when" guide. The bottom line: ask what the next step assumes — a fixed interval means normalize, mean 0 and sd 1 means standardize, and if the model splits on ordering, neither. #DataScience #MachineLearning #Statistics #Normalization #Standardization #DataPreprocessing ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

I found a great resource for interactive learning about machine learning and AI – VizLearn. You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change. It's free and doesn't require registration. https://vizlearn.in https://t.me/MachineLearning9