TechDogs-"A Complete Guide On Artificial Neural Networks"

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A Complete Guide On Artificial Neural Networks

By TechDogs

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Do you remember the movie Chappie about the robot that becomes alive? It is a science fiction film that takes place in future Johannesburg. In the movie, robots called Scouts help fight crime. One Scout named Chappie gets stolen and reprogrammed to have feelings and think on its own. Chappie acts like a child and tries to survive in the dangerous city.

The movie examines concepts of artificial intelligence, consciousness and emotional bonds between humans and machines. This relates to Artificial Neural Networks, which are computing systems inspired by the biological neural networks in brains.

Like Chappie develops consciousness from reprogramming, Artificial Neural Networks can learn and adapt through processing training data. The goal of neural networks is to mimic some properties of biological brains to perform tasks like pattern recognition, classification and prediction.

In this article, we’ll closely understand Artificial Neural Networks, their types and their applications. Read on to know more!
TechDogs-"A Complete Guide On Artificial Neural Networks"
Think about this. Your brain is reading this article but do we know how the brain works? It has neurons - nerve cells that get input from your senses. The neurons process the input and connect to other neurons to pass along the output.

While our brains are natural neural networks, Artificial Neural Networks are modeled after the neurons in human brains. They are important today because we have this huge amount of data to deal with.

According to Statista, global data reached almost 100,000 petabytes monthly in 2017. By 2025, it's expected to be 181 zettabytes! That's 181 trillion gigabytes. With people, organizations and devices generating so much data, neural networks can help extract meaning from it all.

Just like how our brain's neural network processes information, Artificial Neural Networks can analyze massive datasets and identify patterns that would be impossible for human brains. So, these brain-inspired systems allow us to handle today's explosion of data!

Before you ask – how would it help us? Let’s first understand Artificial Neural Networks closely.

What Are Artificial Neural Networks?

Artificial Neural Network, also known as ANN, is a crucial machine learning tool. As their name hints, they are modeled after the human brain's neural networks. Neural networks have input and output layers and usually a hidden layer. This hidden layer transforms the input into something the output layer can use. Neural networks are great at finding patterns that are too complex for human programmers to identify.

TechDogs-"What Are Artificial Neural Networks?"-"Neural Network Architecture"

We learn from our mistakes and experiences, right? Artificial Neural Networks function in a similar way. By analyzing many examples, they learn to recognize patterns and make predictions beyond what rule-based programs can do. This brain-inspired approach makes them powerful machine-learning tools.

Even then, you might ask – how do they learn, though – learn to identify these patterns and make predictions? Read on.

How Artificial Neural Networks Work?

Artificial Neural Networks are trained on data sets. For example, if you want your ANN to recognize a cat, it will show thousands of images of cats so the network can learn what cats look like. After this training, the neural network tries to label new photos as either having a cat or not.

Its labels are checked against human-provided labels to see if it is right or wrong. If the neural network is wrong, a process called backpropagation is used to adjust its learning. Backpropagation tweaks the connection weights (numerical values associated with the connections) in the network to improve accuracy. This entire process of training, testing and adjusting is repeated until the neural network can reliably recognize cat photos with minimal errors.

Imagine how our brain’s neural networks must be processing all the data and the experiences we perceive. Pretty crazy, right?

TechDogs-"How Artificial Neural Networks Work?"-"A GIF Of A Man Saying - Good Brain"
If that amazes you, wait for the types of artificial neural networks.

Types Of Artificial Neural Networks

There are many different types of artificial neural networks. Each has its unique architecture and approach to mimicking human learning. Let's go over five common types:
  • Feedforward Neural Network

    This basic neural network has data flowing in one direction from the input to the output layer, with optional hidden layers in between. There's no looping back of data. The network just propagates forward to make predictions.

  • Convolutional Neural Network

    CNNs have weighted connections like feedforward networks but also special convolutional layers. These apply a convolution operation to the input to extract features before passing them along to the next layer. CNNs are great for processing images and speech.

  • Modular Neural Network

    This is a collection of smaller independent neural networks that each focus on a sub-task and get unique inputs. Together, they output the complete solution. Modular networks break up a complex problem into more manageable pieces.

  • Radial Basis Function Neural Network

    These networks use radial basis functions that consider an input's distance from a center point. They map inputs into functions in the first layer and then compute outputs in the second layer. These are often used to model underlying trends in data.

  • Recurrent Neural Network

    RNNs feed the output of a layer back into itself as input for the next step. This creates a feedback loop and memory so the network can learn sequences and make predictions based on previous context. Helpful in processing text or speech.

Not too complicated to understand, right? These various types of neural networks lead to some real-life applications that we use in our routine days. Unbelievable, right?

TechDogs-"Types Of Artificial Neural Networks"-"A GIF Of A Woman Screaming - This Is Unbelievable!"  

Applications Of Artificial Neural Networks

There are various applications of Artificial Neural Networks in real life. Here are our top picks:
  • Social Media

    Ever wonder how Facebook knows "people you may know" to connect you with? How can apps recognize your face in photos? Neural nets are social spiders that analyze your data and give you recommendations.

  • Marketing And Sales

    Like how Amazon recommends buys based on your browsing history? How does Netflix suggest new shows you'll probably love? Neural nets are the little elves peeking at your data and using it to personalize suggestions just for you!

  • Healthcare

    Neural networks are the eagle-eyed doctors, able to analyze medical scans and spot cancerous tissues. Their super-vision can identify rare diseases early to give patients the best treatments.

  • Personal Assistants

    Ever chatted with Siri or Alexa? The voice behind your personal assistant is a neural network processing your words and formulating coherent responses! Surprising?

  • Image Recognition

    Neural nets are the detectives of the digital world, able to scrutinize images and pick out key details. This helps self-driving cars analyze visual data to navigate roads. It also powers facial recognition and satellite image analysis.

You see, Artificial Neural Networks are the backbone behind many applications we use in our daily routines. On that happy note, let’s conclude this article.

In Conclusion

"I will protect you."

Chappie might have thought to himself while saving the people from crime. Like Chappie, Artificial Neural Networks aim to help, not harm. These artificial models of neural networks hold tremendous potential for progress. Neural networks can empower machines to assist humans in transformative ways across healthcare, transportation, security and more.

As Thomas Edison said, "Our greatest weakness lies in giving up." So let us persist in developing neural networks responsibly, to create a brighter future for all.

To dive deeper into the fascinating world of AI technology and discover the latest insights, advancements and innovative applications, click here now!

Frequently Asked Questions

What Are Artificial Neural Networks And How Do They Relate To Human Brain Functions?

Artificial Neural Networks (ANNs) are computing systems modeled after the neural networks present in the human brain. These networks consist of interconnected nodes or neurons, organized into layers that process input data and produce output. Just as neurons in the brain communicate to process information, ANNs use algorithms to learn from vast amounts of data, enabling tasks such as pattern recognition, classification and prediction. ANNs serve as powerful tools in machine learning, mimicking the brain's ability to analyze complex datasets and derive meaningful insights, contributing to advancements in various fields.

How Do Artificial Neural Networks Learn And Adapt?

Artificial Neural Networks learn through a process of training on labeled datasets. For instance, if an ANN is tasked with recognizing cats in images, it will be fed thousands of cat images to learn what features characterize a cat. During training, the network adjusts its internal parameters, known as connection weights, to minimize errors in its predictions. This adjustment process, called backpropagation, fine-tunes the network's ability to identify patterns and make accurate predictions. Through iterative training and testing, ANNs gradually improve their performance, demonstrating a capacity to adapt and learn from experience, akin to human learning mechanisms.

What Are Some Common Types Of Artificial Neural Networks And Their Applications?

Artificial Neural Networks come in various types, each with unique architectures and functionalities suited to specific tasks. Common types include Feedforward Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), among others. CNNs excel in image and speech processing, while RNNs are adept at handling sequential data like text or speech. These networks find applications across diverse domains, including social media recommendation systems, personalized marketing, healthcare diagnostics, virtual assistants and image recognition for autonomous vehicles. By leveraging the capabilities of ANNs, businesses and industries can enhance decision-making, improve efficiency and innovate in ways that were previously unimaginable.

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