This repository is a collection of Jupyter notebooks that cover a variety of topics on Deep Learning implemented in python. The notebooks meant to be for beginners as well as practitioners who want to have an overview of deep learning topics in an interactive computing environment (Jupyter). While coding the algorithms/functions, I'll try my best to explain the theory behind each step and include some derivations as well.
The following links are to Jupyter Notebook Viewer (nbviewer). Please find the actual notebooks are here.
- Coding Deep Neural Network from Scratch
- Parameters Initialization
- Gradient Checking
- Regularization
- Dropout
- Optimization Algorithms
I highly recommend downloading the notebooks to be able to work in your computing environment. Having the notebook locally allows you to run the code and see how is everything related to each other and maybe change some values to see how that would change the algorithm's output.
P.S. I'll try to add more notebooks and materials in the near future.