Tutorial by Examples

The code listing below attempts to classify handwritten digits from the MNIST dataset. The digits look like this: The code will preprocess these digits, converting each image into a 2D array of 0s and 1s, and then use this data to train a neural network with upto 97% accuracy (50 epochs). "...

The goal of backpropagation is to optimize the weights so that the neural network can learn how to correctly map arbitrary inputs to outputs. Each layer has its own set of weights, and these weights must be tuned to be able to accurately predict the right output given input. A high level overview ...

Activation functions also known as transfer function is used to map input nodes to output nodes in certain fashion. They are used to impart non linearity to the output of a neural network layer. Some commonly used functions and their curves are given below: Sigmoid Function The sigmoid is a ...

Softmax regression (or multinomial logistic regression) is a generalization of logistic regression to the case where we want to handle multiple classes. It is particularly useful for neural networks where we want to apply non-binary classification. In this case, simple logistic regression is not suf...