How Neural Networks Work: A Beginner's Guide

We are living in the era of Artificial Intelligence(AI), and Neural Networks are at the heart of AI. These powerful models mimic the workings of the human brain and are hence called ‘neural networks’. They can perform complex tasks without human intervention, which were previously impossible for computers. Neural Networks are the foundation of technologies like Natural Language Processing, Computer Vision, and Generative AI.

In this guide, we’ll break down how neural networks work, and their importance in emerging technologies.

What are Neural Networks?

Neural Network is a subclass of Machine Learning and it resembles the working of our brain. As our brain has a unit structure called neurons, Neural Networks consist of similar smaller units, namely perceptrons.

A perceptron consists of one or many inputs, bias/weights, an activation function, and an output. You can see the picture describing the perceptron.

As you can see the perceptron receives the input multiplied by weight and after passing it into the activation function gives the output.

Now we will see the overall structure of a Neural Network consisting of different layers. It comprises of three layers:

• Input Layer: It receives the input data and sends it to the next layers to perform calculations and predict the output.
• Hidden Layer: This layer performs computations and extracts patterns from the input data.
• Output Layer: This layer is for the output that our whole network gives.

For example, it can be between 0 and 1 if our model is trained for binary classification.

Next, we will delve into the functioning of Neural Networks and some popular Neural Networks.

Working of Neural Networks

We will understand the workings of Neural Networks. Usually, neural networks are segregated into four categories.

1. Forward Propagation: The input is passed through all the layers, while passing through the layer, at each neuron the weights and activation function are applied to produce the corresponding output.
2. Loss Calculation: In this, loss(the difference between the predicted and actual target value) is calculated. Common loss functions like MSE(Mean Squared Error) are used for regression problems.
3. Back Propagation: The network propagates backward and adjusts the weights and biases to minimize the loss. Techniques like Gradient Descent are used for this process.
4. Weights Update: All three processes above are performed for many iterations(also called epochs) and weights are updated to minimize the loss. The image below will help clarify things.

Now that we have understood what exactly Neural Networks are and how they work, let's see some popular ones. 

Types of Neural Networks

1. Convolutional Neural Networks (CNNs): These networks are designed to process grid-like data such as images. These models are used in Autonomous Vehicles, Image Recognition, etc.

2. Recurrent Neural Networks (RNNs): It is used for sequential data as text or time-series data. A few examples where RNNs are used are Weather Forecasting, Stock Price Prediction, etc.

3. Generative Adversarial Networks (GANs): This type of Neural Network is used to generate new data from a given dataset. A few areas where we can see GANs in practice are creating high-resolution images and synthesizing new videos.

Conclusion

Neural Networks are transforming the power of AI and have become an integral part of our lives in one way or another. Whether you are a beginner or an experienced professional, understanding, learning, and exploring neural networks and AI can open countless opportunities. In the future, neural networks will continue to play a crucial role as one of the key technologies driving advancements in AI.

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