Very basic neural network implementation to understand & try out machine learning. In this repository you can find two neural network classes, one without & one with biases. Both work mostly the same and support the same methods so they will referred to as the neural network. I recommend using the bias version for better results.
The neural network class is only used for construction, forward & backward propagation! To train the network sebig3000/Optimization is needed!
Construct a new neural network by calling its constructor and specifying its parameters. These include the number of inputs, the number of nodes in every layer and the activation functions of every layer & their derivatives. Common activation functions & their derivatives are already implemented in ActivationFunction. Now you can already forward datasets through the network & do back propagation.
Input and output is stored in matricies in which every row represents an dataset and every column gets feed into an node (e.g. element[1][2] is a parameter of a dataset with index [1] and gets feed into the node with index [2])
To train the network you create a new NeuralNetworkOptimizer and give it your neural network. hen call whatever optimization algorithm you like with your input & output sets and let the optimizer train the network. (Adam is recommended)
This repository depends on sebig3000/Matrix! So make sure you have already installed it. Then you can simply download this repository and add it to your project as a new package! Done!
The two people who inspired me to try making my own machine learning project are Brandon Rohrer and Stephen Welch. Both make awesome YouTube videos that explain how machine learning works.
Stephen Welch:
- YouTube: https://www.youtube.com/user/Taylorns34
- Homepage: http://www.welchlabs.com/
- GitHub: https://github.com/stephencwelch
Brandon Rohrer:
- YouTube https://www.youtube.com/channel/UCsBKTrp45lTfHa_p49I2AEQ
- Blog: https://brohrer.github.io/blog.html
- GitHub: https://github.com/brohrer
- Fix dependencies
- Remove optimization wrappers
- Argument validation
- Documentation
MIT License
Copyright (c) 2019 Sebastian Gössl
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