/mathematical-methods-in-deep-learning-ipython

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).

Primary LanguageJupyter NotebookMIT LicenseMIT

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: Ensure to use Python3 to run the notebooks

Table of Contents