What is this?
It is a community-built, Chinese-language collection of Jupyter notebooks covering math foundations, data structures, machine learning, deep learning, LLMs, agents, AI infrastructure, speech, and data science, aimed at job-hunting ('autumn recruitment') interview prep in the LLM era.
It is a community-built, Chinese-language collection of Jupyter notebooks covering math foundations, data structures, machine learning, deep learning, LLMs, agents, AI infrastructure, speech, and data science, aimed at job-hunting ('autumn recruitment') interview prep in the LLM era.Is this a code library or a learning resource?It is a structured knowledge base of notebooks, not an installable library — currently 23 topic modules containing 232 teaching notebooks that keep growing.
It is a structured knowledge base of notebooks, not an installable library — currently 23 topic modules containing 232 teaching notebooks that keep growing.How do I start working through it?Clone the repo, read the markdown explanation and intuition first, then run each notebook cell by cell, tweak a parameter to see the effect, and try reimplementing the core function yourself before checking the answer.
Clone the repo, read the markdown explanation and intuition first, then run each notebook cell by cell, tweak a parameter to see the effect, and try reimplementing the core function yourself before checking the answer.Is the content finished and authoritative?No — the author explicitly calls it a continuously-evolving public learning base, not a finished textbook or 'standard answer', so inaccuracies and gaps are expected and the module/notebook counts in the README are just snapshots.
No — the author explicitly calls it a continuously-evolving public learning base, not a finished textbook or 'standard answer', so inaccuracies and gaps are expected and the module/notebook counts in the README are just snapshots.How is the content organized?It is split into 23 numbered modules (00–22) progressing from math and data structures through ML, deep learning, LLMs/agents, AI-Infra, CV, NLP, speech, time series, recommendation systems, big data, and graph neural networks, each with its own folder of notebooks.
It is split into 23 numbered modules (00–22) progressing from math and data structures through ML, deep learning, LLMs/agents, AI-Infra, CV, NLP, speech, time series, recommendation systems, big data, and graph neural networks, each with its own folder of notebooks.Does it focus on theory or hands-on code?Both — it prioritizes from-scratch implementations (sorting, DP, SVM, PCA, GBDT, CNN, RNN, DQN, PPO, Transformer, KV Cache, FlashAttention, etc.) alongside math derivations, with fixed random seeds and numerical checks for reproducibility.
Both — it prioritizes from-scratch implementations (sorting, DP, SVM, PCA, GBDT, CNN, RNN, DQN, PPO, Transformer, KV Cache, FlashAttention, etc.) alongside math derivations, with fixed random seeds and numerical checks for reproducibility.Why did the author build this instead of using scattered tutorials?Existing algorithm/AI material is spread across papers, code, and interview questions and goes stale fast as LLMs and agents evolve, so the author built one continuously-updated, verify-as-you-go path instead.
Existing algorithm/AI material is spread across papers, code, and interview questions and goes stale fast as LLMs and agents evolve, so the author built one continuously-updated, verify-as-you-go path instead.Is there a recommended learning path?Yes — the README outlines four tracks (algorithms & ML fundamentals, LLM applications/training, AI engineering & systems optimization, and perception/embodied intelligence), with the full staged plan in ROADMAP.md.
Yes — the README outlines four tracks (algorithms & ML fundamentals, LLM applications/training, AI engineering & systems optimization, and perception/embodied intelligence), with the full staged plan in ROADMAP.md.What are similar projects?nlp_course, DeepLearning, llm_interview_note, ailearning, ai-agent-book, maths-cs-ai-compendium, ML-NLP, happy-llm
nlp_course, DeepLearning, llm_interview_note, ailearning, ai-agent-book, maths-cs-ai-compendium, ML-NLP, happy-llmWhich topics is it in?algorithms-and-data-structures, ai-infrastructure, recommendation-system, leetcode-solutions, scikit-learn, time-series, moe, big-data, operating-system, llm-agents, numpy, lora
algorithms-and-data-structures, ai-infrastructure, recommendation-system, leetcode-solutions, scikit-learn, time-series, moe, big-data, operating-system, llm-agents, numpy, lora