Neural Network Lib

Member: 🌜 Ymanzi 🌛

Challenge

Implement a Multilayer Perceptron Library from scratch

Structure

Perceptron (FeedForward)

Backpropagation Equations

Loss Functions Implemented

• Cross Entropy
• Means Squared Error

Activation Functions Implemented

• Sigmoid
• Tanh
• ReLU
• SoftMax

Regularization Implemented

Regularization is a technique which makes slight modifications to the learning algorithm such that the model generalizes better. This in turn improves the model's performance on the unseen data as well, avoiding overfitting.

• L1/L2 Regularization : L1/L2 regularization try to reduce the possibility of overfitting by keeping the values of the weights and biases small.
• Dropout : To apply DropOut, we randomly select a subset of the units and clamp their output to zero, regardless of the input; this effectively removes those units from the model.

• Dropout Connect : We apply dropout with the weights, instead of nodes

Resources

• Activation Functions
• Neural Network Tutorial
• Sigmoid Activation
• Weight initialization
• He Initialization
• Dropout Regularization
• Dropout vs Dropout Connect
• Momentum Intuitive Explanation