A Chinese engineer's field manual for deep learning engineering

What it does

dl_note is a personal knowledge base of markdown notes on computer vision and large language models. The repository organizes free study materials into seven broad tracks: math and programming fundamentals, neural network primitives (convolution, BN, activations), classic CNN backbones, training “alchemy” (data augmentation, regularization, hyperparameters), model compression (pruning, quantization, distillation), and inference deployment (ncnn source analysis, CUDA GEMM, ARM NEON). Think of it as a technical blog frozen into a Git repository.

The interesting bit

Most note repositories are scattered cheat sheets; this one attempts a full-stack curriculum. It even includes source-code walkthroughs of the ncnn inference framework and handwritten notes on heterogeneous computing, topics rarely found in a single, sequential learning path.

Key highlights

• Covers the entire pipeline from math basics through training, compression, and edge deployment.
• Includes detailed breakdowns of classic backbones (ResNet, DenseNet) and efficient variants (MobileNet, ShuffleNet, RepVGG).
• Dedicates a full section to model compression: pruning, knowledge distillation, and quantization.
• Contains inference-optimization notes such as Conv+BN fusion and matrix-multiplication kernel analysis.
• The README doubles as a landing page for a paid LLM inference framework course, so free notes and commercial content share the same space.

Caveats

• All content is written in Chinese; there is no English translation.
• The README mixes free educational notes with advertising for a separate paid course.
• It is a collection of markdown explanations rather than a runnable library or framework.

Verdict

Worth bookmarking if you read Chinese and want a structured, end-to-end study guide for deep learning engineering. Skip it if you are looking for a ready-to-run codebase or English-language documentation.