How accumulatemore/cv works
This repository is a collection of Jupyter learning notes, not an executable application. The user workflow is to study a topic through the notebooks, progressing from foundational concepts to model families and applied tasks. The notes cover PyTorch basics, broad deep-learning and CV/NLP topics, Andrew Ng course material, and LLM/Agent topics. The README explicitly maps video series to notebook ranges; no source excerpts were provided, so relationships beyond those documented mappings are omitted.
Main entry points: 103_Pytorch加载数据.ipynb PyTorch Fundamentals · 202_数据操作、数据预处理.ipynb DL Foundations · 302_课程1_第2周a_神经网络基础之逻辑回归.ipynb Neural Network Courses · 403_基于向量的语义检索RAG.ipynb RAG Concepts
Components 18
PyTorch Notes
- PyTorch Fundamentals
103_Pytorch加载数据.ipynb - Model Training
114_搭建小实战和Sequential使用.ipynb - Training Workflows
119_完整模型训练套路.ipynb
Deep Learning Notes
- DL Foundations
202_数据操作、数据预处理.ipynb - Vision Models
220_经典神经网络LeNet.ipynb - Vision Tasks
234_实战Kaggle比赛图像分类CIFAR10.ipynb - Sequence Models
246_序列模型.ipynb - Attention Models
259_注意力机制.ipynb - BERT
264_BERT预训练.ipynb
Course Notes
- Neural Network Courses
302_课程1_第2周a_神经网络基础之逻辑回归.ipynb - Course Assignments
310_课程1_第1周_作业题_numpy构建基本函数.ipynb - CNN Courses
329_课程4_第1周_卷积神经网络基础.ipynb - Sequence Courses
342_课程5_第1周_循环神经网络RNN.ipynb
LLM Agent Notes
- RAG Concepts
403_基于向量的语义检索RAG.ipynb - LangChain RAG
405_使用LangChain构建RAG.ipynb - LLM APIs
407_PythonAPI环境安装.ipynb - Multi-turn Chat
409_多轮对话.ipynb
Other components
- Learner
Connections 4
- Learner → PyTorch Fundamentals: studies notes
README.md - Learner → DL Foundations: studies notes
README.md - Learner → Neural Network Courses: studies notes
README.md - Learner → RAG Concepts: studies notes
README.md