Artificial neural networks are the basics of Artificial intelligence. These networks are similar and based on the neuron network model of our brain. However, the system cannot yet compete with the human brain as we can imagine, inspire, and use common sense that systems can’t. In this article, we will try to understand the concept of artificial neural networks. That means learning how advanced systems use artificial neural networks to find solutions for the errors and learn independently.
Artificial Neural Networks
The functionality and components of the artificial neural networks are the same. Just as our brains use neural networks to learn from mistakes, artificial neural networks also consist of input and output layers. The neurons contain a hidden layer that transmits the input to the output by finding the complex pattern and converting it into something that output can use. When a neuron makes a guess or decision, it transfers to the other neurons with complete information so that the neuron can correct the first neutron’s output and learn new solutions. In deep learning, artificial neural networks consist of three to ten hidden layers increasing the accuracy.
Types of Artificial Neural Networks
Different artificial neural networks are unique because of the ability to solve errors with different complexity levels. For instance, the most-used network is the feedforward neural network that transmits information in a single direction. However, a recurrent neural network is another widely popular option. These neural networks transmit information in various directions. Suppose you want to perform complex tasks such as language recognition or learning handwriting. In that case, you can use these artificial neural networks because they can display more learning capabilities in a short time.
How Artificial Neural Networks Works
Cognitive neuroscientists spend a lot of time understanding how human neural networks work. They follow the same pattern as to how our brain is well-developed and interconnected in finding solutions and learning. They observe the arrangement of neurons in our brain in hierarchy and process different types of information. For instance, when the input receives some information that the brain considers to change, it then transfers to higher weighted neurons. This means the information divides into various lower weighted connections of neurons in various chunks. Each neuron processes different information of the same error and shares insights to the senior level of neurons for further complex processing.
Artificial neural networks perform their process through various layers of mathematical representation so that the information has some logic to it. The data that the network will use to learn is transferred into the input of one of the million artificial neurons. These neurons are units and have a layer-like arrangement. Almost every neuron connects to the other neurons. The connection is weighted, which defines which neuron will receive the information. Once the input receives the data, it transfers the information to the hidden unit, converting the data to use it. When the data travels through neurons, every hidden unit in the neuron will keep on solving the error. This is how the learning process works.
Learning Process of Artificial Neural Networks
The information that the artificial neural network receives is in abundant quantity. These sets of information are the training set. If you want the program to recognize the difference between a car and a bus, you will share numerous pictures of a car so that system starts to learn and recognize what cars look like for future reference.
When the machine is in the learning process, the output will compare the machine’s result with the description or source you provide. If the output is different, the machine will use a backpropagation algorithm and adjust what it learns. So the system will share the input, and the hidden layers will adjust the information through mathematical equations. The output will then receive the information, compare the result with the source, and transfer it to different neurons until the information is correct. This process is deep learning. That is why systems are more intelligent is recognizing.
Components of Artificial Neural Networks
1. Input Layer
Input is a node that collects the information from the outside into the neuron. They help to transmit the information to the hidden layer for the learning process.
2. Hidden Layer
The hidden layer transforms the data that enters through the input layer and transmits it to the output layer. There is only one hidden layer in a simple artificial neural network. However, deep learning requires more than three hidden layers from complex learning.
3. Output Layer
This layer receives information from the hidden layer and computes the possible output. You can consider output as a result of the input that the neuron receives.
Artificial neurons are mathematical functions that the algorithm uses for the learning process. A neuron takes the information as an input and calculates by multiplying it with the weights. Then, the data transfers to the other neurons.
5. Weight Space
Weight is the connection between the units. Weight space is a parameter that converts the input data into the result by multiplying with the weight. Then, it transfers the processed data to other neurons in the artificial neural networks through the output layer.
6. Forward Pass and Back Pass
In the forward pass, the algorithm will forward propagate the variables in the artificial neural networks. However, in the backward pass, the algorithm will backpropagate the errors to find the output.
Backpropagation is the algorithm that learns through the error by tuning the weights. This process makes the system reliable by reducing errors.
8. Error Function
The main reason for the algorithm is to minimize the error. The function that helps minimize errors in the error function.
From the information in this article, you now have a basic idea about what artificial neural networks are and how they function to minimize the program’s error through artificial intelligence. These networks can learn various activities such as text summarization, picture captioning, animal recognition, and language or writing recognition.