The definitive guide to Neural Networks
By: Nandana Sreeraj
Tags: Neural Networks, Machine Learning, Deep Learning, Artificial Intelligence, Artificial Neural Network

The most simplest and appropriate way to define a Neural Network, more precisely, an Artificial Neural Networks(ANN) was proposed by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielsen. He defines a neural network as:

“…a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.”

An Artificial Neuron Network (ANN) can be understood as a computational model based on the structure and functions of biological neural networks. It is very similar to an artificial human nervous system that receives, processes, and transmits information in terms of Computer Science.

In order to explore more on Neural Networks, Let us see what builds a neural network. To get a more clear idea, let us know in detail about a neuron.

Neurons, the building blocks

A neuron is a unit that takes inputs, does some math with them, and produces an output. Here’s what a 2-input neuron looks like:

Let us see what happens here.

First, multiply each input by a weight

Next, we need to add all the weighted inputs together along with a bias b

Finally, we pass the sum to the activation function.

This is the basic structure of a neuron. Neurons are the building blocks of neural networks. Now, you might have got an idea about a single neuron. Now let us understand a network of neurons.

NEURAL NETWORKS

A neural network is a bunch of neurons connected together. Here’s what a simple neural network might look like:

We have 3 different layers in a neural network:-

Input Layer Hidden Layers Output Layer

Input Layer

The Input layer deals with the communication between the external environment and the neural network. This input is transferred to the hidden layers. Every input neuron will represent an independent variable that has an influence over the output of the neural network.

Hidden Layer

The hidden layer is the intermediate layer which is a collection of neurons having activation function applied to it. It is found between the input layer and the output layer. Its job is to process the inputs obtained by its previous layer and extract the required features from the input data.

Output Layer

The output layer of the neural network collects and transmits the information in a way it has been designed to give. The pattern presented by the output layer can be directly traced back to the input layer. The number of neurons in the output layer should be directly related to the type of work that the neural network was performing.

How to implement a Neural Network?

A neural network can be executed in 2 steps :

1. Feedforward:

Talking about feedforward neural networks, we have a set of input features and some random weights. This sets up the base of a neural network.

2. Backpropagation:

During backpropagation, we calculate the error between the target output and the predicted output. After that, we can use different algorithms such as gradient descent to update weight values.

Why do we need backpropagation?

While designing a neural network, we should train the model and assign specific weights to each of the inputs. That weight that we assigned decides how vital that feature is for our prediction. The higher the weight, the greater the importance. However, we do not know the specific weight required by those inputs in the initial period. So random weights are assigned to our inputs, and our model calculates the error in prediction. Thereafter, we update the weight values we choose and rerun the code. This is known as backpropagation. After individual iterations, we will get lower error values and higher accuracy.

The gist of Neural Networks

Take required inputs Add bias to it Assign weights randomly to the input features Run the code for training and note the error in prediction. Update the weights by means of an algorithm. Repeat the training phase with the new weights. Get the predictions.

How to train a Neural Network?

Design the model and calculate the error in prediction See if the error is acceptable If an error is acceptable, the model is ready for prediction Else, update the weights and train again

Photo by Ilya Pavlov on Unsplash

Conclusion

The neural network is the real stimulus behind the existence of the field of deep learning. Here, ’deep’ refers to the multiple layers of a neural network. It also dominates the contemporary research literature in machine learning and many other fields involving data classification and prediction. Understanding neural networks play a vital role in your first step to deep learning. The magical field has yet many more mysteries to be unsolved. Hope you will dive deeper into the deep learning field.