Neural Network Machine Learning

Neural Network Machine Learning: A Neural Network is a method in artificial intelligence that instructs AI to process data in a way inspired by the structure and functioning of biological neural networks in human brains. The neural network consists of interconnected nodes that can process and learn from available data.

Pattern recognition and decision making in machine learning are possible due to the neural networks of artificial technology. In this article let us explore more about neural networks and their applications in artificial intelligence.

What is a Neural Network?

Neural Network in machine learning, also known as artificial neural network or neural set is a method that extracts features from data. It is inspired by the workings of human brains.

Neural networks are collections of processing units called “Nodes” that are used to pass data to others which is used in decision making, pattern recognition, forecasting, facial recognition, real time translation, function mapping, and more in machine learning and artificial intelligence.

Neural Networks take the input data, train data to recognise patterns, and generate output with a new set of similar data.
Neural networks form the base of deep learning and are a subfield of machine learning.
Convolutional Neural networks are used to conduct 3 dimensional pattern recognition in artificial intelligence and machine learning.

Neural network machine learning consists of neurons, connections, weights, biases, functions and rules. Let us suppose there is an input feature (x) and one output label (y). The simplest one layer neural network with one neuron will have two following parameters.

One weight parameter
One bias parameter

The output value of a single layer one dimensional neural network with one input feature can be evaluated in a single step

y = w*x + b

Now, let us take a number of input features, say from x1, x2,x3,……..,xn. Now expanding the activation function by matrix multiplication of w by x we get the regression equation below.

y = w1x1 + w2x2+ . . . . . . . + wnxn +b

A neuron in a neural network receives the output of the previous layer as inputs which is then transformed by other layers.

Evolution of Neural Network Machine Learning

Let us mark some of the important years behind the evolution of neural network machine learning.

Year	Contribution
1795	Introduction of the method of least squares for linear regression, used for planetary movement prediction.
1805	Publication of the method of least squares.
1943	First non-learning computational model for neural networks.
1949	Hebbian learning rule proposed, based on neural plasticity.
1954	Computational machines simulating Hebbian networks.
1956	Creation of other neural network computational machines.
1958	A Perceptron model of an artificial neural network was developed.
1960	Discussion of multilayer perceptrons with adaptive hidden layers.
1965	The first deep learning algorithm, the Group Method of Data Handling, was published.
1967	First multilayer perceptron trained by stochastic gradient descent.
1969	Introduction of the ReLU (Rectified Linear Unit) activation function.
1969	Perceptrons are deemed incapable of processing the XOR problem.
1971	Description of a deep network with eight layers trained via regression analysis.
1979	Introduction of the Neocognitron, an early version of a convolutional neural network (CNN), without backpropagation.

Neural Network Machine Learning Examples

For example, image classification using convolutional neural networks (CNNs) is a perfect example of neural network machine learning. Input data consisting of various animals is collected (labeled image), preprocessed based on various criteria, and a CNN model architecture is defined where the model is trained using labeled examples.

The classification error found is minimised using backpropagation weight adjustment. The output model can classify the animal and predict whether the animal in the image is a cat or a dog.

Significance of Neural Networks

Neural Networks find many applications in machine learning and artificial intelligence. It can be used by computers to make smart data driven decisions with negligible human assistance. They are self-learning models which can easily set up relations between input and output data and produce effective results.

Neural networks in machine learning are powerful enough to make generalizations and interference with unstructured data. They can make general observations without any explicit training. A neural network is better as it can easily recognize patterns, unlike traditional algorithms.

For instance, when a large dataset of labeled images is trained, the neural networks can easily identify any subtle features which are not visible to the human eye. It can easily recognise two different input sentences having similar meaning.

Can you tell which animal this is in the picture?
What is the name of the animal in the image?

A neural network can easily interpret that both sentences or queries mean the same thing.

Also, check, How AI and Machine Learning are transforming Computer Science?

Working of Neural Networks

Neural Networks are inspired by the human brain and its functioning. Like human brains, it consists of a large number of units of “neurons” which receive, process, and transfer data and information. These neurons work together to solve an advanced computing problem.

There are three layers of neural network machine learning given below as follows.

Input Layer

A collection of information enters into the neural network and forms the input layer. Input nodes in the neural networks categorise and process the information and pass it to the next layer.

For example, in the above image classification example of animals, each pixel of the image corresponds to a neuron.

Hidden Layer (Training Data)

The hidden layer is a layer between input and output layer where the transformation of data based on weighted connection and function takes place.

This layer extracts features and pattern information from the input layer. There can be more than one hidden layer in an artificial neural network.

Output Layer

The output layer is the final layer in the neural network machine learning. It produces and displays the output predictions.

It consists of single or multiple nodes or binary results. There can be more than one neuron if it is a multi classification task.

Types of Neural Network in Machine Learning

There are three major types of neural networks in machine learning. Check them below.

Feedforward Neural Network (FNN): It is the simplest type of neural network model where data is processed in one direction. The input directly enters the hidden layer and generates unidirectional output. It uses a feedback process to make improvements in predictions over time.
Convolutional Neural Networks (CNNs): This neural network model is used for computer vision tasks such as image processing, image segmentation, etc. They are frequently used in neural network models in machine learning.
Recurrent Neural Networks (RNNs): This neural network is designed especially for sequential data such as natural language, and time series. It is used for speech recognition and language modeling tasks in machine learning.

Applications of Neural Network Machine Learning

Neural networks find applications in various fields. Check some of the most important applications of neural networks.

1. Facial Recognition

Majority of devices can now use face recognition methods to predict the age of a person by correlating the lines and spots on a person’s face and other facial features. Convolutional Neural Networks (CNNs) are used in this application of neural networks. It uses various facial features such as face alignment, shape, etc to extract features and predict the age of a person.

2. Forecasting

Neural networks are trained to understand the patterns and detect the possibility of a certain event such as the possibility of rainfall, a rise in stock price of a company, etc with high accuracy.

3. Music Composition

Neural networks can easily study patterns, learn music, and train themselves enough to compose a fresh tune. It can analyse the style of one piece of music and apply it to the other which helps in creating new compositions.

AIVA and Amper Music are some of the popular music AI-powered composition platforms.

4. Identifying Objects

With the help of neural networks, we can easily identify an object within an object by creating a model by training a large dataset of labelled images which can easily study the structure and other features to identify the object in an image.

5. Social Media

Neural Networks are used in social media to recommend friends based on their connectivity. You might have seen the “People you know” feature which suggests people that you might be aware of in real life.

6. Marketing and Sales

Product recommendations, sales strategies, and personalised marketing is made possible with the help of neural network machine learning. The majority of E-commerce platforms use product recommendations based on user activities.

Artificial intelligence can easily track the activities of users such as likes, dislikes, shopping history, etc to tailor a personalised recommendation.

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Neural Network Machine Learning FAQs

Q1. What is an artificial neural network?

Ans: A neural network is a machine learning model inspired from the neural structure of human brains. It is an interconnected group of nodes similar to neurons in brains. It helps in solving complex decision problems and solutions of mathematical problems.

Q2. What is the use of neural networks?

Ans: Neural network machine learning has applications in various fields such as decision making, pattern recognition, forecasting, facial recognition, real time translation, function mapping, and more

Q3. What are ANN and CNN Neural Networks?

Ans: ANN is a general purpose neural network used for a variety of tasks related to classification, regression, and pattern recognition. While CNN is a type of neural network used for computer vision tasks like image classification, object detection, image segmentation, etc.