This repository will help you master neural networks. It contains writeups of how different neural networks work, along with full implementations of different network types.
- The
explanations
folder has writeups of each algorithm. - The
nnets
folder has clean implementations that are best for someone who understands the high level concept. - The
exploration
folder has implementations that are more exploratory and easier to understand for beginners.
I recommend reading the writeup, then looking at the exploratory implementation, then looking at the clean implementation.
Gradient descent is an important building block for neural networks.
- Gradient descent with linear regression tutorial
- Clean implementation - linear regression is equivalent to a dense network with no activation function.
- Notebook implementation
Dense networks are networks where every input is connected to an output.
- Dense network tutorial coming soon
- Clean implementation
- Notebook implementation
Convolutional neural networks are used for working with images and time series.
- Convolutional network tutorial coming soon
- Clean implementation
- Notebook implementation
pip install -r requirements.txt