What is a neural Network

Introduction

Have you ever wondered how we recognize faces, understand languages, and make decisions? It’s fascinating to think about how our brains work, and scientists and engineers have created something equally amazing called neural networks.

In this article, we’re going to embark on an exciting journey to unravel the mysteries of neural networks, their connection to our brains, and how they are transforming the world around us.

What is a Neural Network?

Simply put, a Neural network is a machine-learning algorithm inspired by the human brain. The brain processes information by passing information from one neuron to the next and building neural pathways. These pathways determine things like your particular memory, how your hand should move, or what to say next.

Similarly, a neural network is made up of a series of nodes that are connected. Each neuron/node takes in a number of inputs and produces an output. The outputs of the neurons are then combined to produce the final output of the neural network.

Neural networks can be used to solve a wide variety of problems and are particularly well-suited for problems that involve pattern recognition.

As you continue to read the article, you will learn about the use of neural networks in various industries, so let’s continue!

How Neural Network works

Neural networks work by learning from data. When you train a neural network, you give it a set of data and the desired output. The neural network then tries to find a set of weights that will allow it to produce the desired outcome for any input in the data set.

Once the neural network is trained, it can be used to make predictions on new data. Let us explain using an example: if you train a neural network to classify images of cats and dogs, you can then use the neural network to predict whether a new image is of a cat or a dog.

Here is a simple analogy that you can use to understand how neural networks work:

Imagine you are trying to teach a child how to identify different types of animals. You start by showing the child pictures of different animals, and you tell the child what each animal is. The child will then learn to associate certain features with certain animals. For example, the child might learn that animals with four legs and fur are likely to be dogs.

As the child continues to learn, they will start to build a mental model of what different animals look like. This mental model will allow them to identify new animals they have never seen.

Now you know that neural networks work similarly. They are trained on a set of data, and they learn to associate certain features with specific outputs. Once trained, they can be used to identify new data they have never seen before.

Break-down of a neural network

Let’s explore the fundamental components and processes that make neural networks function:

Neurons

Artificial neurons, also known as nodes or units, are at the core of a neural network. These neurons are inspired by the neurons in our brains and serve as the building blocks of the network. Neurons receive inputs, perform computations, and produce outputs.

Neurons are organized into layers within the neural network.

Layers

The three main types of layers are:

1. Input layer: The starting point of the network that receives the initial data or input.
2. Hidden layers: Intermediate layers between the input and output layers. They perform complex computations and extract relevant features from the data.
3. Output layer: The final layer that produces the desired output or prediction based on the processed information.

Neural networks can have many hidden layers, and the number of neurons in each layer can vary. You can decide the size of the layers and the number of neurons assigned if you decide to create your own neural network. The number of hidden layers and neurons in each layer is determined by the problem the neural network is trying to solve.

Connections and weights

Neurons in one layer are connected to neurons in the next layer through connections. Each connection is associated with a weight, which represents the strength or importance of the connection.

These weights determine how much influence a particular neuron has on the neurons in the next layer. Initially, these weights are assigned random values, but they get adjusted during the training process to optimize the network’s performance.

Bias

As you can see in the diagram attached above, bias is a value that is added to the output of each neuron. The bias helps to prevent the neural network from becoming too sensitive to small changes in the input data.

Activation Function

In the above diagram, you can see the activation function is clearly labelled. An activation function is a mathematical function that is used to transform the output of a neuron into a value that can be used by the next neuron in the neural network.

Activation functions are essential because they allow the neural network to learn non-linear relationships between input and output data.

What is a non-linear function, you may wonder? Well, a non-linear relationship is a relationship between two variables where the change in one variable does not correspond to a constant change in the other variable.

Many different activation functions can be used in neural networks. The choice of activation function depends on the problem the neural network is trying to solve.

Feedforward Propagation

When data enters a neural network, it flows through the layers in a process called feedforward propagation. Each neuron receives inputs from the previous layer, multiplies them by their corresponding weights, sums them up, and applies the activation function. This process continues layer by layer until the output layer produces the final result.

Backpropagation

Backpropagation is an algorithm used in machine learning to train neural networks. Backpropagation works by starting at the output layer of the neural network and working its way back to the input layer. The error is calculated at each layer and then used to update the weights and biases of the neurons in that layer.

The backpropagation algorithm is repeated until the error between the neural network’s output and the desired output is minimized.

As you know, that error-minimization leads to a more efficient and robust algorithm that has made neural networks a powerful tool for machine learning.

Real-life Applications of a Neural Network

Neural networks have found their way into numerous real-life applications, revolutionizing industries and enhancing our daily lives. Let’s explore some exciting examples of how neural networks are making a tangible impact:

Image and Object Recognition

Neural networks have significantly advanced image recognition capabilities. They can identify and classify objects, faces, and scenes with remarkable accuracy. Do you know which technology powers the smart devices that use facial recognition systems? Neural Networks, of course.

It also enables automatic tagging and categorization of images on social media platforms, making it easier to search and organize vast collections of photos. For example, tagging your albums as food, people, text, outdoors etc.

Natural Language Processing

Neural networks play a vital role in natural language processing (NLP), enabling machines to understand and generate human language.

You may have used language translation services, chatbots, and voice assistants like Siri and Alexa, but did you know that they heavily rely on neural networks to comprehend and respond to your queries?

Moreover, sentiment analysis, which determines emotions expressed in text, also benefits from neural networks, helping companies gauge public opinion on their products or services. For example, after your online purchase of a product, you leave a review saying: “Good quality product and fast delivery!.” The company will use NLP to determine your review and then conclude that the review was a positive one.

Autonomous Vehicles

We have all heard of Teslas and self-driving cars, but the major reason behind the development of self-driving cars is neural networks. These networks process vast amounts of sensor data, such as images, lidar, and radar, to recognize objects, detect pedestrians, and make split-second decisions for safe navigation.

Neural networks enable vehicles to perceive the environment, anticipate potential hazards, and drive autonomously, promising increased safety and efficiency on our roads. Now, you know the science that goes behind a self-driving car.

Not only this, the Neural network has extensive usage from the medical field to robotics, virtual reality, cybersecurity, and environmental monitoring. It won’t be wrong to say that Neural Networks might help us shape a new future.

Future of Neural Networks

Neural networks have come a long way, but the future holds even more exciting possibilities. Let’s discuss some advancements that we can look forward to.

Explainable AI

In the future, you can expect to witness an explainable AI, which provides insights into the decision-making process of neural networks. This means that you will know how an AI reaches a conclusion and does all the back-end work. This will help gain trust in AI systems and ensure transparency, especially in critical applications such as healthcare and autonomous vehicles.

We have discussed previously how industries and gadgets rely on neural networks. There might be a time in the future when we can deploy neural networks on edge devices. This means that AI computations will occur locally on devices like smartphones, smartwatches, and autonomous drones, reducing the reliance on cloud computing. How cool is that?

While neural networks offer immense potential, they also raise ethical considerations and challenges that must be addressed:

Bias and Fairness

Neural networks learn from the data they are trained on, and if that data is biased, it can lead to biased outcomes. For example, if you have trained a neural network for a four-legged fur animal using cats and dogs, then it may not be able to successfully identify bears and wolves. Ensuring fairness and mitigating biases in training data and algorithms will be crucial to prevent discrimination and promoting equal opportunities.

Privacy and Data Security

Neural networks often require large amounts of data to train effectively. Protecting user privacy and securing sensitive information will be paramount as neural networks become more integrated into our daily lives. Striking the right balance between data collection for training and respecting individual privacy will be an ongoing challenge.

Conclusion

Neural networks are like the brainpower behind the scenes, bringing technology to life in incredible ways. After reading the article, you know that neural networks have the potential to revolutionize industries, enhance our everyday experiences, and even inspire new career paths.

With neural networks, the possibilities are endless. So, get ready to embrace a future with Codingal’s Data Science for kids course to immerse yourself in and learn about a world where intelligent machines work side by side with us, making our lives easier, more efficient, and maybe even a little bit magical. The future is bright, and neural networks are at the heart of it all.