In this playlist, I teach the neural network architecture and the learning processes to make the ANN able to learn from a dataset.
The Tutorials are divided in each part of the neural network and we start coding it in C++ in Visual Studio 2017. Once you have completed the tutorial you will be able to design your own neural network and optimize it.
- Neuron & Layer
- Input & Output Layer
- Hidden Layer
- Neural network
- FeedForward & Backpropagation
Download theory paper: Backpropagation
C++ Source code uploaded in GitHub: Neural Network Tutorial
: weight for node in layer for incoming node .
: bias for node in layer .
: product sum plus bias (activation) for node in layer .
: output for node in layer .
: number of nodes i layer .
: activation function for the hidden layer nodes.
: activation function for the output layer nodes.
where ranges from to , number of nodes in the next layer.