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Python code (in the form of Jupyter ipython notebooks) to support the book "Math and Architectures of Deep Learning" (Krishnendu Chaudhury with Ananya Ashok, Sujay Narumanchi, Devashish Shankar).
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README.md

Math and Architectures of Deep Learning

Python code (in the form of Jupyter ipython notebooks) to support the book "Math and Architectures of Deep Learning".

Code contributors: Ananya Ashok, Sujay Narumanchi, Devashish Shankar, Krishnendu Chaudhury.

This repository contains the example code - mostly in Numpy and PyTorch - corresponding to the theoretical topics introduced in the book. The code listings are organized in chapters that correspond to the main book.

Installation

  1. Clone the repository: git clone https://github.com/krishnonwork/mathematical-methods-in-deep-learning-ipython.git
  2. Create virtual environment: virtualenv venv --python=python3 (you may need to do pip install virtualenv first)
  3. Activate virtual environment: source venv/bin/activate
  4. Change directory: cd mathematical-methods-in-deep-learning-ipython
  5. Install dependencies: pip install -r requirements.txt
  6. Navigate to the python directory: cd python
  7. Start jupyter: jupyter notebook

This will redirect you to a browser window with the ipython notebooks

Note: Setup works with both python2 and python3

Table of Contents

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