Yuan Lab袁静课题组

Stanford's classic CV course — the full deep-learning vision stack from image classification to detection and segmentation, with public lecture notes.

An open interactive deep-learning textbook with runnable code, adopted by 500+ universities; available in multiple languages.

The classic top-down free course — you train a working model in lesson one.

MIT's annually updated one-semester intro covering deep-learning fundamentals through generative models; all videos and slides public.

Google's free crash course with visual exercises for building core ML concepts quickly.

Harvard's most popular course — solid programming and CS fundamentals for absolute beginners.

Official free courses on Transformers, LLM fine-tuning and agents — directly relevant to LLM research.

A beginner's guide to isolated environments with conda, choosing the right PyTorch build for your GPU, and verifying the install.

A widely-adopted one-stage object detector with polished engineering, thorough docs and a low reproduction barrier — a good entry point for detection projects.

OpenMMLab's modular detection toolbox supporting a large family of detection and segmentation algorithms — suited for systematically reproducing and benchmarking methods.

An attention-based network for P/S-wave arrival picking, with released pretrained weights, sample data and a Google Colab notebook for end-to-end reproduction.

ETH's deep-learning toolkit for seismology, providing a unified interface to pretrained models and benchmark datasets for tasks like detection and phase picking.

Official PyTorch example suite covering image classification, DCGAN, reinforcement learning and more — concise reference implementations for reproducing core workflows.

A unified interface to a large family of NLP and multimodal pretrained models, with thorough docs and examples for reproducing Transformers and fine-tuning workflows.

A sample reproduction guide for the Project Practice section, demonstrating the step-by-step layout from environment to run — to be replaced by a real flow for a specific project.

MIT's open archive of full courses — lecture notes and exams freely available.

Official tutorials from tensors to deployment — the first stop for both API reference and patterns.

The famed interactive ML journal (archived, fully readable) — several articles on attention and visualization are classic primers.