Dive into Deep Learning: Chinese Code and Exercises
A Chinese deep learning project snapshot combining textbook explanations, mathematics, runnable code and exercises, with environment and reproducibility notes.
What this archive covers
This project connects concepts, mathematics and runnable code in one learning path. It starts with data manipulation, linear algebra, probability and automatic differentiation, then moves toward linear models, multilayer networks, convolution, recurrent structures, attention and optimization.
Treat the snapshot as one environment
Read the included installation notes and create an independent Python environment. The notebooks and dependencies belong to a particular project snapshot; mixing them with a system environment or a different framework version can produce misleading failures.
Learn through controlled experiments
Reproduce the original example first, then change one variable such as learning rate, batch size or network depth. Record code snapshot, dependencies, random seed, device, data preparation, training curve and validation result instead of keeping only a final metric.
Maintenance note
Training metrics do not prove generalization, and a teaching benchmark is not a business performance promise. Model, data set and dependency terms should be checked independently. Content review date: 2026-08-23.
Save to your cloud drive
Save the complete collection first so files remain together and are easier to access across devices.
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Dive into Deep Learning study guide
Read the prerequisites, reproduce a small example in an isolated environment and change one experiment variable at a time while recording conditions.
Before you start
- Know Python basics and have introductory linear algebra, calculus and probability knowledge.
- Prepare an independent Python environment and enough storage for the selected chapter.
- Evaluate GPU and data requirements before running large models or data sets.
Quick start
- 01
Read setup notes first
Follow the snapshot's environment instructions and avoid mixing system Python or unrelated dependency versions with the notebooks.
- 02
Start with foundations
Work through data operations, linear algebra, calculus, probability and automatic differentiation before moving to larger networks.
- 03
Run and change one example
Reproduce the original notebook, change one parameter or preprocessing step and compare training curves, metrics and runtime.
- 04
Save experiment conditions
Record code, dependency versions, random seed, device and data preparation so the result can be reproduced later.
Usage tips
- Use the environment notes that match the archive snapshot instead of assuming current dependencies are interchangeable.
- Separate training, validation and test data when evaluating generalization.
- When memory is tight, reduce batch size, input size or model size while keeping data checks intact.
Troubleshooting and uninstall
Why does a notebook open but fail during execution?
Check the active kernel and independent environment, then verify dependencies, data paths and device selection rather than focusing only on the final error line.
Why do my metrics differ from the material?
Hardware, framework versions, randomness, data versions and preprocessing can change values. Fix the conditions and compare trends before exact numbers.
Frequently asked questions
Is this only a machine learning algorithm cheat sheet?
No. It is a Chinese Dive into Deep Learning project with explanations, mathematics, diagrams, code and exercises.
What background is useful before studying it?
Python plus basic linear algebra, calculus and probability is useful, and the early chapters help build those foundations.
Is a GPU required?
Basic chapters and small examples can often run on a CPU; larger networks and data sets benefit from a GPU depending on the chapter.
Why are identical examples not producing identical metrics?
Initialization, framework version, hardware, data version and preprocessing can differ, so compare only after fixing the experimental conditions.