Machine Learning

Write NumPy code in the familiar way, and then run it on GPUs and TPUs. Automatic differentiation and data batching will be handled automatically, without requiring any extra effort from you. - Automatically compute derivatives of standard Python and NumPy functions. - Compile pure functions using jit to run them on hardware accelerators. - Combine grad, jit, and vmap in any order and scale your computations across multiple devices. https://github.com/jax-ml/jax 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 ⭐️ J…

🚨 SURPRISE ALERT! 🚨 Stop paying full price on Udemy. Seriously. 💸 I built a bot that hunts down 100% FREE Udemy coupons 24/7 — while you sleep, eat, or scroll. 🎯 Here's the magic: 📚 Mini App catalog — every active free coupon in one place 🔔 Auto-push — new courses land straight in your chat 📢 Live channel — never miss a deal Why it matters? Most people pay $200+ for courses you can grab for $0 — if you know where to look. Now you have a bot that does the looking for you. ⚡ 🎓 Try it now: ht…

🔖 Interactive CNN Layer-by-Layer Explanation With the CNN Explainer, you can learn about: 🫡 Input RGB channels; 🫡 Convolution; 🫡 ReLU; 🫡 Max Pooling; 🫡 Activation maps; 🫡 Flatten; 🫡 Softmax; 🫡 Final prediction. You can upload your own image and see how the network processes it step by step. ⛓ https://poloclub.github.io/cnn-explainer/

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, ag…

🔖 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/

Machine Learning pinned a photo

🔥 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 practic…

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…

📚 7 Best Websites to Learn Computer Science Subjects for Free More: https://enghusseinsheikho.substack.com/p/7-best-websites-to-learn-computer?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

https://substack.com/@husseinsheikho?utm_source=share&utm_medium=android&r=3hwnr8 Follow me to more posts and useful content

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 blo…

"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 …

🔖 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://enghusseinsheikho.substack.com/p/a-comprehensive-resource-for…

Machine Learning pinned a photo

🚀 RESEARCH & DIGITAL SOLUTIONS — YOUR PARTNER IN SUCCESS! Are you a researcher, student, business owner, or developer looking for professional support? We are here to help you turn your ideas into real results! 💡 🔬 1. Scientific Research & Academic Publishing Professional review and evaluation of scientific manuscripts before submission. Guidance on journal selection and publication strategies. Expert advice to improve research quality and presentation. Support for researchers throughout the ac…

Channel photo updated

📎 Media

Channel photo updated

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 mana…