A simple sample for "Neural Networks and Deep Learning"

• Notice these codes are written before taking Machine Learning couse and mastering Numpy and so I might edit them ASAP.

This is a sample implementaion of the "Neural Networks and Deep Learning". The Neural-networks-and-deep-learning package is the very basic implementation and the Neural_Network_Scikit package is the Neural Networks and Deep Learning implementation using scikit_learn and scipy libraries.

Getting Started

To work with the implementaion of Neural Newtwork using Scikit_learn , you have to consider num_samples as the number of rows and num_features as the number of columns of a matrix that will be the input matrix with the shape (num_samples, num_features). To Simplify reading the matrices' shapes, let num_samples be N and num_featrues be p. then matrices that will be used in MLPClassifier will have theses shapes:

• X -> N * p
• w -> p * first_hidden_layer_nurons_number
• y -> N * 1
• b -> N * 1

where w and b are weights and biases matrices that you can see in the simple implementation version. aslo the point is that hidden_layer_sizes used in the number of hidden layers excluding the input and output layers. So if we need 2 hidden layers we will use a tupple (x, u) with x as the number of nurons in first layer and so on.

Prerequisites

• numpy
• scikit-learn
• scipy

Acknowledgments

Special Thanks to:

• 3blue1brown
• http://neuralnetworksanddeeplearning.com