Do you wonder how Google Assistant or Apple’s Siri follow your instructions? Do you see advertisements for products you earlier searched for on e-commerce websites? If you have wondered how this all comes together, it is because of Artificial Intelligence (AI), which works on the backend to offer you rich customer experience. And it is Artificial Neural Networks (ANN) that form the key to train machines to respond to instructions the way humans do.
This article dives deep into the fundamental concepts of neural networks, including:
- What is deep learning?
- What is a neural network?
- How does a neural network work?
- Advantages of neural networks
- Applications of neural networks
- The future of neural networks
A Brief History of AI
The human brain is the most complex organ in the human body. It helps us think, understand, and make decisions. The secret behind its power is a neuron.
Ever since the 1950s, scientists have been trying to mimic the functioning of a neuron and use it to make smarter and better robots. After a lot of trial and error, humans finally created a computer that could recognize human speech. It was only after the year 2000 that people were able to master deep learning (a subset of AI) that was able to see and distinguish between various images and videos.
Before taking a detailed look at what is a neural network, you should be aware of deep learning.
What is Deep Learning?
Deep learning is a subset of machine learning that asks computers to do what comes naturally to humans: learn by example.
The machine gets trained with images as examples. This process is very different from hardwiring a computer program so that it recognizes something and learns. You don’t control how it learns; you control the aspects that go into it. Based on the images that are fed earlier, the computer identifies the object.
Scientists managed to build an artificial form of a neuron(biological) that powers any deep learning-based machine.
After briefly discussing deep learning, let us move on to ‘What is a neural network?’
What is a Neural Network?
To understand how an artificial neuron works, we should know how the biological neuron works.
These receive information or signals from other neurons that get connected to it.
Information processing happens in a cell body. These take in all the information coming from the different dendrites and process that information.
It sends the output signal to another neuron for the flow of information. Here, each of the flanges connects to the dendrite or the hairs on the next one.
The image shown below depicts an ANN.
The network starts with an input layer that receives input in the form of data.
The lines connected to the hidden layers are called weights, and they add up on the hidden layers. Each dot in the hidden layer processes the inputs, and it puts an output into the next hidden layer and lastly, into the output layer.
Looking at the above two images, you can observe how an ANN replicates a biological neuron.
Input to a neuron – input layer
Neuron – hidden layer
Output to the next neuron – output layer
A neural network is a system of hardware or software patterned after the operation of neurons in the human brain. Neural networks, also called artificial neural networks, are ways of achieving deep learning.
Let us discuss how ANN works in the following section of What is a Neural Network article.
How Do Neural Network Works?
Ever asked Siri a question? The device answers accurately. Let us understand how this virtual assistant accomplishes speech recognition.
Consider a neural network shown below:
There are input, hidden, and output layers on the network. The sentence that the network needs to recognize is: What is the time?
Here, each word comes in as a pattern of sound. The sentence gets sampled into discrete sound waves.
Let’s consider the first word: What
You can see the waveform is split based on every letter. Now we will split the sound wave for the letter W into smaller segments.
When we analyze the letter ‘W,’ the amplitude varies in the sound wave, as shown below.
We collect the values at intervals and form an array. Different amplitudes come in for different letters, and we feed the array of amplitudes to the input layer.
Random weights get assigned to each interconnection between the input and hidden layers.
We always start with the random key, as assigning a preset value to the weights takes a significant amount of time when training the model.
The weights get multiplied with the inputs, and a bias is added to form the transfer function.