Machine Learning

Est. read time: 1 minute | Last updated: January 17, 2025 by John Gentile

Contents

Overview
- Intro ML
Software and Platforms
References

Overview

It is useful to review linear algebra concepts before continuing.

TBD reference [1]

Intro ML

Software and Platforms

TensorFlow

TensorFlow is one of the most popular platforms for Machine Learning development, protoyping and deployment.

TensorFlow Docker Container with GPU Support

Follow instructions at python-lib/tensorflow to install a Docker container with NVIDIA CUDA GPU support and common Jupyter & SciPy libraries.

TensorFlow Development

Other TensorFlow Resources

instillai/TensorFlow-Course: some simple tutorials and Jupyter notebooks on getting started with TensorFlow.
TensorFlow Hub: repository of hundreds of trained, ready-to-deploy machine learning models.

PyTorch

Jupyter Notebooks

For more info, see SciPy distribution in Python about installing Jupyter notebook support.

Another great tool which comes with most all dependencies/libraries ready to go is Google Colab, which allows you to store (in Google Drive), edit and run (even on GPU & TPU servers in some instances) Jupyter notebooks in the Google cloud environment.

Other Tools

Horovod: Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet
MLPerf: ML benchmark results for various computing platforms.
Caffe 2 is now a part of PyTorch

References

[1]I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016 [Online]. Available at: http://www.deeplearningbook.org

Dive into Deep Learning — Dive into Deep Learning 0.15.1 documentation
MIT Deep Learning 6.S191
Deep Learning
[Practical Deep Learning for Coders Practical Deep Learning for Coders](https://course.fast.ai/)
Neural networks and deep learning
GitHub - josephmisiti/awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software.
NVIDIA Deep Learning Examples
GPT in 60 Lines of NumPy