Neural Network in Machine Learning
Introduction
Back in school days, you must have learned about the Neuron System in our body, and that made you fascinated to know more about it. Just like that the Artificial Neural Network is the modern-day neuron system for machines. With the modernization of artificial intelligence, ANN has proved to be one of the most useful equipment in machine learning algorithms.
Here, we are going to cover various aspects of Artificial Neural Networks and their role in Machine learning.
What is an Artificial Neural Network?
Artificial Neural Network is a form of machine learning that is designed keeping in mind the human brain as its key model. In other words, just like how our nervous system works by analyzing previously-stored memories and acting accordingly, the same way ANN learns from the data and makes its assumptions subsequently.
Nonlinear statistical models, or ANNs, are nonlinear statistical models that demonstrate a complicated relationship between inputs and outputs to uncover a new pattern. Artificial neural networks are used for a range of tasks, including image identification, speech recognition, machine translation, and medical diagnosis.
Training data is used by neural networks to learn and increase their efficiency over time. However, once these learning algorithms have been fine-tuned for accuracy, they become formidable tools in artificial intelligence and computer science, allowing us to quickly categorize and cluster data. When compared to manual identification by human specialists, tasks in speech recognition or picture recognition can take minutes rather than hours. Google’s search algorithm is an example of a well-known neural network.
Structure
A simple neural network is made up of three layers of linked artificial neurons:
Input layer
The artificial neural network’s input layer receives data from the outside world. The data is processed, analyzed, or categorizes, and then sent onto another layer by input nodes. The input layer accepts data in the form of words, numbers, audio files, picture pixels, and other formats.
Hidden layer
The input layer, as well as other hidden levels, provide input to hidden layers. A considerable number of hidden layers may be found in artificial neural networks. Each hidden layer examines the output of the previous layer, further processes it, and then sends it on to the next layer.
Output Layer
The output layer displays the ultimate outcome of the artificial neural network’s data processing. It can be made up of one or more nodes. If we have a binary classification issue, for example, the output layer will also have one output node that will return a 1 or 0. If we have a cross-classification issue, however, the output layer may contain several output nodes.
Processing
Traditional machine learning approaches require human input in order for the program to function properly. The list of relevant features that the program must assess is manually determined by a data scientist. This restricts the software’s capabilities, making it difficult to design and administer.
The neural network learns from these datasets over time, providing the correct answer in advance. After the network has been trained, it begins to make educated estimates about the ethnicity or mood of a fresh image of a human face that it has never seen before.
Applications of ANN
The applications of Artificial Neural Networks are listed below.
- Signature Classification
We employ artificial neural networks to recognize signatures and categorize them according to the person’s class while developing these authentication systems. Furthermore, neural networks can tell if a signature is authentic or not.
- Recognition of Handwritten Characters
ANNs are used to recognize handwritten characters. Handwritten characters can be in the form of words or digits, and neural networks have been taught to identify them.
- Facial Recognition
We employ neural networks in order to recognize faces depending on the identification of the individual. They are most typically utilized in situations where security access is required. The most common form of ANN utilized in this sector is convolutional neural networks.
- Speech Recognition
Speech recognition primarily relies on artificial neural networks (ANNs). Earlier voice recognition approaches relied on statistical models such as Hidden Markov Models. With the advent of deep learning, several types of neural networks have become the sole means to obtain an accurate categorization.
Limitations
You must also know about the limitations of the ANN –
- Because the structure of a neural network is distinct from that of microprocessors, it must be imitated.
- Large neural networks need a long processing time.
- Parallel computing power is required for the creation of Artificial Neural Networks.
- When the network’s failure on the sample is lowered to a specific value, the training is complete.
Conclusion
These were a couple of facts you must know about how Artificial Neural Network works and how it contributes to machine learning. It makes the work easier using its AI features and the algorithms it uses. We hope this article has helped you to learn about all the aspects of ANN
