handson-mlp
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, PyTorch, and Hugging Face libraries.
File Explorer
Download Latest Version (.zip)- bug_report.yml
- clarification-request.yml
- config.yml
- feature-or-improvement-request.yml
- Dockerfile
- README.md
- california.png
- ladybug.png
- .dockerignore
- .gitignore
- .python-version
- 01_the_machine_learning_landscape.ipynb
- 02_end_to_end_machine_learning_project.ipynb
- 03_classification.ipynb
- 04_training_linear_models.ipynb
- 05_decision_trees.ipynb
- 06_ensemble_learning_and_random_forests.ipynb
- 07_dimensionality_reduction.ipynb
- 08_unsupervised_learning.ipynb
- 09_artificial_neural_networks.ipynb
- 10_neural_nets_with_pytorch.ipynb
- 11_training_deep_neural_networks.ipynb
- 12_deep_computer_vision_with_cnns.ipynb
- 13_processing_sequences_using_rnns_and_cnns.ipynb
- 14_nlp_with_rnns_and_attention.ipynb
- 15_transformers_for_nlp_and_chatbots.ipynb
- 16_vision_and_multimodal_transformers.ipynb
- 17_speeding_up_transformers.ipynb
- 18_autoencoders_gans_and_diffusion_models.ipynb
- 19_reinforcement_learning.ipynb
- Appendix_A_autodiff.ipynb
- Appendix_B_mixed_precision_and_quantization.ipynb
- Appendix_C_support_vector_machines.ipynb
- Appendix_D_relative_positional_encoding.ipynb
- Appendix_E_state_space_models.ipynb
- apt.txt
- CHANGES.md
- index.ipynb
- INSTALL.md
- LICENSE
- math_differential_calculus.ipynb
- math_linear_algebra.ipynb
- ml-project-checklist.md
- pyproject.toml
- README.md
- tools_matplotlib.ipynb
- tools_numpy.ipynb
- tools_pandas.ipynb
- uv.lock
π Installation Guide
1. Get the code
git clone https://github.com/ageron/handson-mlp
Downloads the entire project code from GitHub to your computer.
cd handson-mlp
Moves into the project folder you just downloaded.
2. Docker
Easy RecommendedPrerequisites
- Git Needed to download the project code from GitHub.
- Docker Desktop Needed to build and run containers. Install it and keep it running in the background.
docker build -f docker/Dockerfile -t handson-mlp .
Builds a runnable image based on the Dockerfile.
docker run -p 8080:80 handson-mlp
Runs the built image as an actual container.
Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.
3. Python
EasyPrerequisites
pip install .
Installs the package published on PyPI directly β no need to clone the source.
jupyter notebook
Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.
If it runs without errors and prints output in the terminal, it worked.
// repository documentation
Was this content helpful?
(0 ratings)