This repo contains a project i did during my second year in college. I wanted to have a deeper understanding of how gradients were calculated and backpropagated through the Network and I felt like if I could implement backprop of a Convolutional Nerual Net that would help me immensely.

The project can be divided into 3 parts:

• Optimizers.py

• ConvNetModule.py

• Layers.py

Contains four Algorithms for optimization

• SGD
• Adam
• Momentum
• RMSProp

Controls the training process, interacts with the layers and sends the gradients to the optimizers.

The hardest part to implement. Contains forward and backward operations for all the layers.

Additional Libraries used: Numpy(matrix operations), tqdm(appearance) and Matplotlib(plotting).

There are still some things that need work and can be found here.

For help with understanding the Backward operation of Convolutions:
https://github.com/JeyrajK/convolutional-neural-networks/blob/master/Back%20propagation%20of%20cnn.ipynb
https://towardsdatascience.com/forward-and-backward-propagations-for-2d-convolutional-layers-ed970f8bf602
https://cs231n.github.io/
https://www.jefkine.com/general/2016/09/05/backpropagation-in-convolutional-neural-networks/
https://towardsdatascience.com/backpropagation-in-a-convolutional-layer-24c8d64d8509