If you have any interest in the field of machine learning and artificial intelligence (AI), then you must have come across neural networks. I like to think of a neural network as a computer program that learns how to accomplish specific tasks with the help of lots and lots of data. Probably a very common [...]

# Category: Neural Networks Basics

## Neural Networks Learning The Basics: Gradient Descent and Stochastic Gradient Descent

Overview In the previous post we looked at backpropagation. In the example we calculated the gradient of the loss with respect to each weight parameter. The gradient determined which direction to move the weight parameter and how much to move it by. The new weight parameter was then updated using the formula below: $latex w_{new} [...]

## Neural Networks Learning The Basics : Layers, Activation

This post continues from neural network basics part 1: Layers Matrix Multiplication. This post therefore assumes that you have basic knowledge on what a neuron is. Why Add an Activation Function? In neural network basics part 1: Layers Matrix Multiplication I covered matrix multiplication and defined a simple neural network as the weighted sum of inputs with the equation: [...]

## Neural Networks Learning The Basics : Layers, Matrix Multiplication

This blog post takes a close look at the first fundamental concept of a neural network that I introduced briefly in my previous post. The layers. Layers are the building blocks of a neural network and contain the networkâ€™s knowledge. In order to achieve our goal one needs to first understand what a neural network [...]