Tensor methods in Python with TensorLy

This repository contains a series of tutorials and examples on tensor learning, with implementations in Python using TensorLy, and how to combine tensor methods and deep learning using the MXNet, PyTorch and TensorFlow frameworks as backends.

Installation

You will need to have the latest version of TensorLy installed to run these examples as explained in the instructions.

The easiest way is to clone the repository:

git clone https://github.com/tensorly/tensorly
cd tensorly
pip install -e .

Then simply clone this repository:

git clone https://github.com/JeanKossaifi/tensorly_notebooks

You are ready to go!

Table of contents

1 - Tensor basics

• Manipulating tensors (unfolding, n-mode product, etc)

2 - Tensor decomposition

• CP decomposition
• Tucker decomposition

3 - Tensor regression

• Low-rank tensor regression

4 - Tensor methods and deep learning with the MXNet backend

• Tucker decomposition via gradient descent
• Tensor regression networks

5 - Tensor methods and deep learning with the PyTorch backend

• Tucker decomposition via gradient descent
• Tensor regression networks

6 - Tensor methods and deep learning with the TensorFlow backend

• Tucker decomposition via gradient descent

Useful resources

The following are very useful sources of information and I highly recomment you check them out:

• TensorLy documentation : extensive documentation, API, etc.
• Deep Learning - The Straight Dope : a great tutorial for Deep Learning using MXNet, by Zack Lipton.
• Deep Learning with PyTorch : another great tutorial, this time with PyTorch, by Soumith Chintala.
• The fast.ai cource : a great course that teaches Deep Learning from the start, and build up all the way to state-of-the-art models.