{"id":19214224,"url":"https://github.com/0101011/bootstrap-ml","last_synced_at":"2025-05-12T22:21:57.075Z","repository":{"id":206491513,"uuid":"162252401","full_name":"0101011/bootstrap-ml","owner":"0101011","description":"A comprehensive collection of pre-written code for machine learning and deep learning use cases, all in one convenient place. 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Whether you're a seasoned practitioner or just starting your ML journey, this repository provides a solid foundation to build upon.\n\n## What Is It About?\n\n**Bootstrap ML** aims to accelerate your machine learning and deep learning projects by providing reusable, well-documented code snippets and notebooks. It covers a range of use cases, from quick starts to advanced neural network implementations.\n\n### Folder Overview\n\n- **0_quick_start**:\n  - `0_logging_device_placement.py`: Logs device placement to help identify performance bottlenecks.\n\n- **1_keras_api**:\n  - `1_numbers_classification.ipynb`: Notebook demonstrating number classification using Keras.\n  - `2_sequential_model.py`: Basic Sequential model example using Keras.\n  - `3_basic_classification.ipynb`: Notebook for basic classification using Keras.\n  - `4_text_classification.ipynb`: Notebook for text classification using Keras.\n\n- **2_estimators**:\n  - `2_1_linear_model.ipynb`: Notebook demonstrating a linear model implementation using TensorFlow Estimators.\n\n- **19_lingvo**:\n  - `19_1_task_config.py`: Task configuration example using the Lingvo framework.\n\n- **20_tf2**:\n  - `20_1_actor_critic_agent.ipynb`: Notebook demonstrating an Actor-Critic agent.\n  - `20_2_a2c.py`: Advantage Actor-Critic (A2C) implementation.\n\n- **777_workarounds**:\n  - `777_1_tf2_cuda10.py`: Workaround for TensorFlow 2.x with CUDA 10 compatibility issues.\n\n## Benefits\n\n- **Plug-and-Play**: Pre-written, reusable code that can be easily integrated into your projects.\n- **Wide Range of Use Cases**: From data preprocessing to advanced neural network models.\n- **Scalable and Efficient**: Optimized for both small-scale experiments and large-scale production workloads.\n- **Customizable**: Easily modify and extend the code to suit your specific needs.\n\n## TODO List\n\n- [ ] Add more examples for TensorFlow 2.x.\n- [ ] Add the most used deep learning architectures with practical examples.\n- [ ] Expand the Lingvo framework examples.\n- [ ] PyTorch models and examples.\n- [ ] Add enchmarking suite for model comparisons.\n\n## Contributing\n\nI've been working on this repo on my free time contributing on and off as I had free time. Here's how you can get involved:\n\n1. Fork the repository.\n2. Create a new branch (`git checkout -b feature-branch`).\n3. Make your changes and commit them (`git commit -m 'Add new feature'`).\n4. Push to your branch (`git push origin feature-branch`).\n5. Create a new Pull Request.\n\nFeel free to reach out for questions, suggestions, or feedback!\n\n-- Andrew\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F0101011%2Fbootstrap-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F0101011%2Fbootstrap-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F0101011%2Fbootstrap-ml/lists"}