Introducing Neural Networks - And why should we care to know?

Neural Networks are a specific subset of machine learning algorithms that work by simulating patterns in our brains and analyzing them to form predictions about data inputted or provided in the future based on previous inputs or outputs, respectively.

Photo by Uriel SC on Unsplash

Right Now, neural networks are the most powerful tool in artificial intelligence. This article will explain to those who don't know about Neural Networks and how it works with Artificial Intelligence. This article will cover what Neural Networks are and what they do with Artificial Intelligence. It also aims to show new people who have no idea what this technology is, how we use it now, and where/how it can grow to. It also details how Neural Networks work with artificial intelligence, show you the different learning types, and all of the possible applications in society that come along with having these technologies within our reach. 

What Are Neural Networks?

Neural networks are a computer system that uses a huge amount of interconnected neurons to process and understand data. It is used heavily in artificial intelligence as it is capable of higher-level thinking skills for computers and machines. Neural networks allow the computer to learn and discover new things without any man-made restrictions and can be applied to many different fields, including medicine and health, robotics, science, finance, etc.

Photo by Sajad Nori on Unsplash

How Do Neural Networks Operate?

There are two ways in which neural networks can operate. They function as either supervised or unsupervised neural networks. The supervised neural networks are those in which two or more human beings formulate a training set and label these sets of data. The unsupervised neural networks are those in which a computer reads the data.

Photo by Innoarchitecht.com

What Are the Types of Neural Networks?

There are numerous different types of neural networks, but three main types are commonly used: feed-forward, recurrent, and hybrid.

What Are the Different Kinds of Neural Networks?

We will be discussing: 

• Feed Forward Neural Networks 
• Recurrent Neural Networks
• Hybrid Neural Networks 

Feed-Forward Neural Network:  A Feedforward network is a form of artificial neural network where there is one input (the variable) to which all other variables are fed back.

Recurrent Neural Networks:  A recurrent network is a form of artificial neural network where one input can connect to multiple outputs simultaneously. 

Hybrid Neural Network: It's a combination of Feed-Forward and Recurrent networks with the benefit that it retains knowledge generated by previous inputs. What this allows for is the creation of systems that are more robust and resistant to noise, while still being able to maintain their ability to learn new information.

What are the Real-world Applications of Neural Networks?

Neural network, as a recent technology is impacting our daily lives as never before. Here, are some of the ways they are helping the world to grow. Note that a lot of other topics are yet to be invented using neural networks, but we all got to agree that utilizing this technology offers real benefits to organizations. for instance, they assist to call in processing complex issues, preventing fraud, and maintain security.

• The finance industry is booming thanks to predictive analytics lately. It helps predict the currency exchange rates and helps in forecasting an outsized kind of volatile investments like stocks and cryptocurrency. Here the neural networks will help in reducing the reiteration of labor, lowering human efforts and false-positive results.
• Social media platforms like LinkedIn use neural networks to detect spam emails and messages. also, these networks assist in seeing appropriate content to make a far better recommendation flow for its users.
• These artificial neurons aid in recognizing the tone and thus the language of the speech often mentioned as voice/speech recognition systems. These networks classify incoming calls into predetermined categories or assign a lead quality score.

That's all for now! If you got to learn something new share this article with your friends! That will help a lot. 

Stay Safe and Creative

-Shiv Shikhar Sinha