Introduction
We have been hearing about AI everywhere and how it will replace humans in many sectors.
But do we really know what it is? And, how is it going to affect the human race?
Nowadays, AI is a commonly used terminology in analytics. It is often used in neural networks or artificial neural networks. Especially, if you are looking to build a career in data analytics.
An algorithm called a neural network allows a computer to learn from observational data. Computing neural networks are modelled on how the biological nervous system processes information.
Interconnected neurons with input-receiving dendrites make up biological neural networks. They generate an output based on these inputs and send it to another neuron through an axon.
History of AI
Let's dive deeper into the history of AI and how it evolved to have a better understanding of it.
Since the 1950s, researchers have been working to create smarter and better robots. To do this, they mimic how neurons function.
Humans ultimately succeeded in building such a computer. A computer that could identify human speech, after much trial and error. Only after the year 2000 were people able to achieve deep learning. It is a branch of AI that can recognize and differentiate between different images and movies.
Deep Learning: Do we know what it is?
Deep learning is a subset of machine learning. It enables computers to learn from examples as people do.
Instead of hardwiring a computer programme to detect and learn, machines are educated using images as examples. You have control over the factors that feed into it, not how it knows. The computer recognizes the object based on the previous input photographs.
Any system using deep learning is powered by a synthetic version of a biological neuron.
Origin of the term "Neural Network"
After learning the history of AI, let's dive into the origin of the term 'Neural Network'.
The earliest prototype of an artificial neural network was created by logician Walter Pitts and neuroscientist Warren S. McCulloch. Their work is where the word 'neural network' originated. They discussed the idea of a neuron in their work. A neural network is a single cell that is part of a network of cells and takes inputs, evaluates them, and produces an output.
Functions of Neural Networks
Neural networks can carry out the following tasks.
Text translation.
Recognize faces.
Identify speech.
Identify text written by hand.
Command robots.
These are just a few examples, but neural networks can perform many more functions.
Types of Neural Networks
We have talked a lot about the history and origin. Now, let's see the types of Neural Networks.
Multi-layer perceptron
The neural network employs a nonlinear activation function and a multi-dimensional perceptron. The multi-dimensional perceptron might have three or more layers.
Convolutional Neural Network
This neural network employs a variant of complex perceptrons and the convolutional neural network.
Recursive Neural Network
It uses weights to produce structured predictions.
Recurrent Neural Network
A recurrent neural network is the fourth one. It links neurons in a specifically directed cycle.
LSTM neural network
LSTM neural network utilizes the recurrent neural network design. This long short-term memory neural network lacks an activation function.
Sequence-To-Sequence Models
The last two modules create a vector space from a large amount of text. It uses two recurrent networks and shallow neural networks.
How do the neural networks work and are put to use?
As discussed earlier, the purpose of neural networks is to find patterns in data. Clustering, classification, and prediction are used to separate these patterns. These all address specific issues that apply to various fields. The fields include marketing, sales, security, and finance.
Neural networks are used for a variety of tasks. It includes facial recognition software to apprehend criminals and daily stock market forecasting.
These networks can be leveraged for marketing objectives. They can be used for chatbots, target marketing, and market segmentation.
Neural networks will be used in biomedical systems in the upcoming years. They will be used to track down diseases. Or even to determine the probability of a person having a particular genetic characteristic or anomaly.
Conclusion
Artificial neural networks come in a variety of forms and functions to yield astounding outcomes. The neural networks are constructed similar to how brain neurons function. It is their most crucial feature.
Consequently, they are built to acquire more knowledge and make decisions in a human-like manner. Thus, neural networks can actually deliver better results. It contrasts typical machine learning algorithms which seem to stop after a certain point.
Data science is used in the real world and creates prediction models to improve business outcomes. This ensured placement program is appropriate for professionals and recent graduates. Anyone who wants to pursue a career in data analytics and science can pursue this degree.
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