#MachineLearning

Day 6 of my CNN journey. After building a CNN from scratch with NumPy and experimenting with TensorFlow/Keras, I moved to PyTorch. This time, I wanted to understand what actually happens inside the training loop. 👇 #MachineLearning #DeepLearning

Deep-Path: The entire Deep-ML catalog, organized into one structured learning path featuring nearly 2,000 machine learning coding problems and hands-on projects. Throughout the path, you'll work through the math, implement concepts from scratch using minimal libraries like NumPy or CUDA, and build projects step by step There's also Deep-Path Essentials, a shorter version with 400 core problems and projects organized into 10 sections, covering the fundamentals of machine learning

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.

One year ago, I launched this free textbook Today, the book has been downloaded by >35,000 students from >500 universities. If each book costs $100, we have saved families $3.5M. Boiler up @LifeAtPurdue #DataScience #MachineLearning

Directional Roaming in Formaldehyde Revealed by Dynamics Simulations on a Machine-Learnt Potential #machinelearning #compchem

LLM Engineer's Handbook Master the art of engineering large language models from concept to production Link - #LLM #MachineLearning #AI #code #Coding #Python #programming

Physical structure as an organizing principle for universal Hamiltonian learning #machinelearning #compchem

🚀 Built a Diabetes Risk Predictor with ML! 🤖 Compared LR, RF & SVM 📈 ROC-AUC: 0.825 (SVM) 🔗 #MachineLearning #Python

What does AI training work actually look like? Reading what a model wrote, deciding if it’s right, and explaining why or why not. Less sci-fi, more careful reading #AI #MachineLearning

diff-soup: GPU-native Differentiable Simulation and Inference for Large-scale Stiff Dynamical Systems #machinelearning #compchem

🌳 From building one decision tree to combining an entire Random Forest, step by step, from data to predictions. How many of these 10 steps can you complete without looking at the code? #MachineLearning #RandomForest

Machine Learning Models Explained: How to Choose the Right One #ml #machinelearning #mlmodels #blog #read #tech #MindInventory

ARMD-Predict: multi-site machine learning to predict antibiotic resistance before culture results #machinelearning #compchem

Exploratory Data Analysis (EDA) is a critical step in any data analysis project. We will explore how to perform EDA with R, focusing on data visualization and summary statistics. #DataScience #machinelearning #dataAnalysts #ArtificialInteligence #coding

This is a smaple ML for Stock Selection using YFinance and opencode. This was done in under 30 mins. Disc: This is a model sample only and not a reco. Stocks shown here are for Demo purposes only. #StockMarket #MachineLearning #AIFinance #Investing #QuantTrading #PythonFinance

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

Want to learn AI & Machine Learning but don't know where to start? 🤖 Simple 6-step roadmap: Python → Math → Core ML → Projects → Deep Learning → LLMs Free courses + a 90-day plan inside 👇 #AI #MachineLearning #LearnAI

Python AI and Machine Learning Projects for Beginners Link - #Python #MachineLearning #AI #code #Coding #Programming

Systems Biology and Machine Learning Identify RAB2A as a Potential Target for Ocimum basilicum L. Volatile Intervention in Alzheimer's Disease via the Nose‐to‐Brain Pathway #machinelearning #compchem

Introduction to Frontiers in materials discovery: innovations and challenges in machine learning and artificial intelligence #machinelearning #compchem