Last updated 1 year ago by Samay Shamdasanipython
Before we get started with the how of building a Neural Network, we need to understand the what first.
Neural networks can be intimidating, especially for people new to machine learning. However, this tutorial will break down how exactly a neural network works and you will have a working flexible neural network by the end. Let’s get started!
With approximately 100 billion neurons, the human brain processes data at speeds as fast as 268 mph! In essence, a neural network is a collection of neurons connected by synapses. This collection is organized into three main layers: the input layer, the hidden layer, and the output layer. You can have many hidden layers, which is where the term deep learning comes into play. In an artifical neural network, there are several inputs, which are called features, and produce a single output, which is called a label.
The circles represent neurons while the lines represent synapses. The role of a synapse is to multiply the inputs and weights. You can think of weights as the “strength” of the connection between neurons. Weights primarily define the output of a neural network. However, they are highly flexible. After, an activation function is applied to return an output.