深入浅出PyTorch

DeepTutor is an open-source, self-hostable AI tutoring platform developed by HKUDS for lifelong personalized tutoring. It provides a web application and command-line interface for interactive learning, problem solving, quiz generation, deep research, visualization, and mastery practice, with persistent memory and learning state shared across knowledge bases, books, notebooks, tutor personas, and other activities. The platform supports multi-agent problem solving, retrieval-augmented learning with source-traced and page-level citations, math animations, interactive visualizations, and tutor bots built from personal study materials. Its knowledge sources include personal documents, EPUBs and books with annotations, GitHub repositories, web search, and connected libraries; documented retrieval and ingestion options include GraphRAG, PageIndex, LightRAG, linked knowledge bases, Obsidian, and configurable parsing and vector backends. DeepTutor also includes courses, research workflows, an ecosystem of MCP services and tool or capability plugins, and integrations with connected coding agents and other partners. It can run locally or through Docker.

The Hugging Face LLM Course is a free educational course covering large language models, natural language processing, and the Hugging Face ecosystem. It teaches Transformer concepts and use of Transformers, Datasets, Tokenizers, Accelerate, and the Hugging Face Hub; later chapters cover classic NLP tasks, model demos, fine-tuning, dataset curation, and reasoning models. The course includes code that can be run in Google Colab or Amazon SageMaker Studio Lab, requires good Python knowledge, and recommends prior introductory deep-learning study. It is released under the Apache 2 license, with contributions supported through issues, translations, and the course repository.

MiniMind is an open-source tiny large-language-model project and end-to-end training tutorial developed by Jingyao Gong. Its main dense model is approximately 64M parameters, and the repository provides the model architecture, tokenizer, datasets, inference code, and training pipeline. The pipeline covers pretraining, supervised fine-tuning, hand-written LoRA, DPO, PPO, GRPO, CISPO, model distillation, tool calling, adaptive thinking, and agentic reinforcement learning. Core algorithms are implemented directly with native PyTorch rather than relying on high-level abstractions from third-party training libraries. Its agentic reinforcement-learning path performs multi-turn rollouts, executes generated tool calls, appends tool observations to the context, calculates trajectory-level rewards, and updates the policy; rollout can use local PyTorch generation or an SGLang server. MiniMind supports dense and mixture-of-experts variants, single- and multi-GPU training, YaRN-based RoPE length extrapolation, Transformers-format models, and inference through llama.cpp, vLLM, or Ollama. The repository also includes a Streamlit chat interface and a lightweight OpenAI-compatible API server with tool-call and reasoning fields. It is released under the Apache License 2.0.

AI Engineering Lab is a free, self-paced 24-week AI engineering course developed by Zorost Intelligence AI Lab. It takes learners from Python and machine learning through deep learning, LLM internals, prompt and context engineering, retrieval-augmented generation with vector search, quantization, LoRA and DPO fine-tuning, evaluation harnesses, coding-agent harnesses, AI agents, MCP, and cloud and Databricks platforms including Azure AI Foundry, Google Vertex AI, AWS Bedrock, and Databricks. The curriculum uses one continuous fictional freight-company case study. Each week pairs a concept with a runnable Python, SQL, or PySpark artifact; from Week 3 onward, the work includes a metric and an error note. The repository contains 43 runnable notebooks, publishes the full curriculum online, requires no signup or paid API key, and states that the first eight weeks require no GPU. It is MIT licensed.

A curated map of modern time-series forecasting architectures, covering classical baselines, linear models, transformers, diffusion models, and pretrained forecasters. It explains the mechanisms and trade-offs of representative approaches and provides compact, runnable PyTorch implementations, with a short README for each architecture.