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.
- Clone the repository:
git clone https://github.com/krishnonwork/mathematical-methods-in-deep-learning-ipython.git
- Create virtual environment:
virtualenv venv --python=python3
(you may need to dopip install virtualenv
first) - Activate virtual environment:
source venv/bin/activate
- Change directory:
cd mathematical-methods-in-deep-learning-ipython
- Install dependencies:
pip install -r requirements.txt
- Navigate to the python directory:
cd python
- 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
-
Chapter 2:
- 2.2 Intro to Vectors
- 2.4 Intro to Matrices, Tensors and Images
- 2.7 Basic Vector and Matrix operations
- 2.12.5 Solving an overdetermined system using pseudo inverse
- 2.13 Eigenvalues and Eigenvectors
- 2.14 Rotation Matrices
- 2.15 Matrix Diagonalization
- 2.16 Spectral Decomposition of a Symmetric Matrix
- 2.17 Finding the axes of a hyper-ellipse
-
Chapter 3
-
Chapter 4
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Chapter 5
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Chapter 6
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Chapter 7
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Chapter 8