This repo mostly serve as educational proposes, project itself is a implementation of one layer neural network (and can also be modified to be convulotional neural network). I made this solely for practicing and help other students understand the concept of machine learning with neural network. (No experience in outside library (numpy, etc.) needed)
First, bear with me through the mathematical model behind this project. It's crucial for understanding and using the library. To better understand back propagation and neural network, I recommend checking out 3Blue1Brown's video series. Feel free to skip this part if you are already familiar with the math behind neural network.
Used Chain Rules to calculate the gradient of our neural network.
These are the derivatives we used to calculate the total gradient.
the sigmoid function and how weights and biases are used.
After knowing the underlying mathematics, check out the documentation for how to use this project. Note: the neural network is not built with matrix, rather with simple for loop and nodes.
This project is licensed under the MIT License - see the LICENSE.md file for details
3Blue1Brown