pytorch is truly peak engineering

Post-training, sometimes called alignment, enables #LLMs to plan, reason, and interact, which pre-training alone doesn't provide. Our latest blog is a primer on post-training for engineers new to LLM modeling, covering SFT, RLHF, DPO, etc. 🔗 #PyTorch

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Copy-pasting PyTorch code is fast — using an AI coding model is even faster — but both skip the learning. That's why I asked my students to write by hand ✍️. 🔽 Download: . After the exercise, my students can understand what every line really does and

Free course to learn full stack perspective on building ML-powered products. Link in comment. Covering everything from neural networks and PyTorch to deployment, monitoring, data management, and ML team management across 18 detailed videos

Massive PyTorch tutorial video is now published on my channel! . Goes from tensor programming basics, automatic differentiation to the implementation ideas behind some of the most useful networks.

Google just dropped a new light-weight 270M model!. Gemma-3-270M is a lightweight, open-weight LLM that's perfect for task-specific fine-tuning with strong instruction-following. This notebook explains how to build Gemma-3-270M from scratch using PyTorch, step by step.

[ML Grind]. > Finished the ML in production course.> Finished the Sequence Models course.> DL & PyTorch: replicating ViT paper.> Working on the NLP with Attention Models: just to learn about transformers .> Reading the Deep Learning for Biology book: predicting masked amino acids

With constant improvements in its optimization passes, PolyBlocks-generated code continues to get better! We compare here against all other state-of-the-art production PyTorch backends on a variety of workloads, including transformer-based and convolution-based ones. The latest

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PyTorch vs TensorFlow: New survey settles the debate. - PyTorch → ~25% faster in training, ~78% faster in eval. Simpler for research. TensorFlow → Stronger in deployment (mobile, browser, server). Read the whole survey here:

When I sit down to optimize some PyTorch code.

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If I were to start learning ai/ml as a software engineer, I’d do the following:. - Python & OOP.- PyTorch.- Machine Learning Specialization (Coursera).- Deep Learning Specialization (Coursera).- StatQuest on YouTube.- Designing Machine Learning Systems.- Hands-On Large Language.

🚀 Launched DevPick !.A one-stop app to explore & compare tech—from React vs. Vue to Postgres vs. Mongo to TensorFlow vs. PyTorch ⚡.👉 Try it: Built with Next.js + Supabase. Feedback welcome ❤️

I'm looking for cracked devs:. - python/ml dev (pytorch, yolo, bytetrack) who can actually ship a full service, not just notebooks.– a fullstack dev (laravel + react) who can turn chaos into product. DM with repos + shipped work and relevant experience only.

From-scratch PyTorch reimplementation of a Gemma3 style 270M transformer: multi-query attention (GQA with n_kv_groups=1), RoPE, Q/K RMSNorm, weight tying. Training loop included. - Attached: code snippet and train/val loss curve from an initial run. - Code: