Bootstrap ML

Bootstrap ML is a comprehensive collection of pre-written code for machine learning and deep learning use cases, all in one convenient place. Whether you're a seasoned practitioner or just starting your ML journey, this repository provides a solid foundation to build upon.

What Is It About?

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.

Folder Overview

• 0_quick_start:

  • 0_logging_device_placement.py: Logs device placement to help identify performance bottlenecks.

• 1_keras_api:

  • 1_numbers_classification.ipynb: Notebook demonstrating number classification using Keras.
  • 2_sequential_model.py: Basic Sequential model example using Keras.
  • 3_basic_classification.ipynb: Notebook for basic classification using Keras.
  • 4_text_classification.ipynb: Notebook for text classification using Keras.

• 2_estimators:

  • 2_1_linear_model.ipynb: Notebook demonstrating a linear model implementation using TensorFlow Estimators.

• 19_lingvo:

  • 19_1_task_config.py: Task configuration example using the Lingvo framework.

• 20_tf2:

  • 20_1_actor_critic_agent.ipynb: Notebook demonstrating an Actor-Critic agent.
  • 20_2_a2c.py: Advantage Actor-Critic (A2C) implementation.

• 777_workarounds:

  • 777_1_tf2_cuda10.py: Workaround for TensorFlow 2.x with CUDA 10 compatibility issues.

Benefits

• Plug-and-Play: Pre-written, reusable code that can be easily integrated into your projects.
• Wide Range of Use Cases: From data preprocessing to advanced neural network models.
• Scalable and Efficient: Optimized for both small-scale experiments and large-scale production workloads.
• Customizable: Easily modify and extend the code to suit your specific needs.

TODO List

• Add more examples for TensorFlow 2.x.
• Add the most used deep learning architectures with practical examples.
• Expand the Lingvo framework examples.
• PyTorch models and examples.
• Add enchmarking suite for model comparisons.

Contributing

I'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:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add new feature').
4. Push to your branch (git push origin feature-branch).
5. Create a new Pull Request.

Feel free to reach out for questions, suggestions, or feedback!

-- Andrew