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.
Linear regression and gradient descent are important building blocks for neural networks.
Dense networks are networks where every input is connected to an output.
Convolutional neural networks are used for working with images and time series.