Machine Learning with Python

Hugging Face Viewer is now at 2300 viewable models! 😊 Would love more feedback and ideas! It's a free interactive graph visualizer for learning about the architectures of open source AI models! 🚀 Hovering nodes in the graph links to a definitions + animation and the paper that introduced it! 🌟 hfviewer.com #HuggingFace #AI #MachineLearning #OpenSource #TechNews #DataViz ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Boost me and we both win! Sign up on Kimi and we each get a guaranteed benefit — up to 1-Year Membership Credits: https://kimi-bot.com/activities/viral-referral/share?scenario=invite&from=share_poster&invitation_code=PJMK9U

16 GB RAM. No cloud subscription. Which local AI model actually fits? How AI Helps built a free Telegram model picker. Choose your task, RAM or VRAM, language, runtime, and commercial-use requirement. Then compare a shortlist by memory, license, sources, download options, and launch commands when available. Join How AI Helps and open the pinned model-picker guide

🔖 A useful training tool for Data Scientists 📊 🫡 Real-world tasks from IT companies; 🫡 SQL practice; 🫡 Python tasks; 🫡 Preparation for Data Science interviews. ⛓ Link to the training tool https://www.stratascratch.com/ 🏷 #DataScience #SQL #Python #InterviewPrep #TechTraining #DataAnalyst ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

U-Net by hand ✍️ ~ 17 steps walkthrough below I consider U-Net as a key milestone in deep learning, the first image-to-image model that really worked! It came out of medical imaging, an unusual place, not from NeurIPS or CVPR or ACL. Now it is the backbone of diffusion models, which you see in almost all modern image generation models. I drew the network as a C so the matrix multiplication flows naturally down. Tilt your head to the right and it is a U again. 🤣 Goal: push a 3 x 16 image down to a 2 x 4 bottleneck and back out again, filling in every cell yourself. = 1. Given = An image of three channels, R, G and B, sixteen pixels wide, and every kernel the network will use. = 2. Convolution 1 = Let us slide the first kernel over the image. Each output is one multiply-and-add over a 2 x 3 window, and the result is the green feature map. = 3. Find the maxima = We circle the largest value in each 1 x 2 window. Circling first is worth the extra step: it is the pooling decision, made before anything is written down. = 4. Max pool 1 = Let us copy those maxima down. Sixteen columns become eight, and half the detail is gone for good. = 5. Convolution 2 = We convolve again with the second kernel, deeper into the contracting path. The feature map is blue now. = 6. Find the maxima again = Same move as step 3, on the blue map. = 7. Max pool 2 = Eight columns become four. = 8. The bottleneck = Let us convolve once more. This is the bottom of the U, a 2 x 4 block that is everything the network kept. = 9. Spread it out = We start back up. The transposed convolution writes each bottleneck value into a wider grid, leaving gaps between them. = 10. Transposed convolution 1 = Let us fill those gaps by convolving over the spread-out grid. Four columns become eight. = 11. The first skip = We copy the encoder's matching row straight across. This is the skip connection, and it is the whole reason a U-Net can recover detail that pooling threw away. = 12. Convol

Google just released a free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 🤖⚙️ 10% → 17:44 - build your first agent 🚀 30% → 39:30 - Loop engineering: iterate, check, break 🔁 60% → 1:12:38 - Graph engineering 🕸️ 75% → 1:34:26 - agents that throttle themselves ⚡ 100% → 1:55:05 - full graph for multi-agentic systems 🏗️ everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph. watch the course, build the graph - then read the full architecture below ↓ More: https://telegra.ph/Graph-Engineering-build-1000-agent-loops-in-one-window-from-one-prompt-full-5-step-course-08-02 #GraphEngineering #AI #Agents #Graphs #Tech #Coding ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A