TechDogs-"The Easiest Guide To Understand Neural Networks "

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The Easiest Guide To Understand Neural Networks

By TechDogs Editorial Team

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Overview

Imagine visiting the Louvre in Paris or the Met Museum in New York and gazing upon the marvelous sculptures and paintings by Da Vinci, Michelangelo, and Vasari. Now, given a block of marble and a chisel, would you be able to carve Michelangelo's David? How about painting a replica of the Mona Lisa? Well, of course not!
 
No one can recreate such masterpieces - even after observing them several times. However, there exists someone who can remember and process information so efficiently that he puts Mike Ross to shame! This guy uses his keen powers of observation to generate remarkably similar outputs or predictions - all in a few minutes. Sounds like an exceptional dude to hang out with, right?
 
Unfortunately, the guy in question is nothing more than a computer model - Neural Networks, as he is known. This article will explain what Neural Networks are, their working, origin, typical application and potential future.
TechDogs-Make Mistakes, Learn From Mistakes.-"The Easiest Guide To Understand Neural Networks"
Let's start by playing a high-stakes game. We'll give you a few seconds to prepare since we caught you off-guard. Ready now?

The rules are simple - all you have to do is look at the images and distinguish which one of these faces is that of an actual human being. Here are the images -
 
Made your choice? Now for the reveal (drum roll, please...). The image on the right is of a real, living, breathing individual. However, the image on the left was generated by a Neural Network after analyzing thousands of human faces. Yes, the child in the left image does not exist! So, how does this incredible technology generate such realistic human portraits, and more importantly, what more can it create?

That is precisely what we will be talking about in this article. Let's dive in then!
 

What Is A Neural Network?


Neural Networks are arrangements of computer algorithms that mimic the working of the human brain to recognize meaningful relationships in large datasets. Neural Nets, as the hip kids call it, can efficiently identify patterns and trends in data that are too vast or complex for humans (or other computers) to analyze and make forecasts using this information. It is essentially an information processing model inspired by biological systems such as the brain - which processes about a billion, billion calculations per second. (That's an 18-figure number!)

However, what exactly do we mean when we say that Neural Networks mimic the working of the brain? A brain cell or neuron is the fundamental unit responsible for receiving and relaying information in the brain. Similarly, in a Neural Network, artificial neurons (called nodes) are connected to create a layer and such layers stack up to form a network, hence the name Neural Network.

Neural Nets can analyze enormous amounts of data, draw meaningful insights from them and produce comparable outputs or predictions - all in a matter of minutes! If you think we are talking about something out of a Black Mirror episode, don't be surprised - we thought so too. However, once we got into the "how" of Neural Networks, it all started making sense.

First - a little detour.


The Origin Of Neural Networks

 
The origin of Neural Networks is hazy. Most agree that it started in 1943 when Warren McCulloch and Walter Pitts published a paper on how neurons in our brain might work. To test their theory, they modeled the first rudimentary Neural Network using electrical circuits. Various attempts were made to improve this model; however, the vital breakthrough came in 1957 as the Perceptron - the first trainable Neural Network. (Yup, Neural Nets need to be trained, as we'll soon see.)

While the Perceptron's design was elementary, having just three layers, experts developed the first state-of-the-art multilayered network in 1975. However, it took them decades to formulate practical training algorithms for it. These algorithms enabled Neural Nets to process complex information in a matter of seconds - and they generated quite a buzz. It might surprise you to know that the First International Conference on Neural Networks in 1987 drew more than 1,800 attendees!

Today, a better understanding of the brain and advancement in data processing capabilities have allowed Neural Networks to be hundreds of layers deep. That's what the "deep" in Deep Learning, the main application of Neural Nets, refers to - the depth of the network's layers.

Enough time travel for now! Let's dive into the impressive, behind-the-curtain stuff.
 

Let's Look At A Neural Network


TechDogs-"Let's Look At A Neural Network"-An Image Showing The Basic Layout Of A Neural Network
So, here's the basic layout of a Neural Network. It consists of three distinct layers - the input layer, the output layer, and sandwiched in between are the hidden layers. For ease of understanding, let's assume a single hidden layer.

Each layer comprises multiple nodes (artificial neurons) that can either receive or relay information. The information travels through connections, which play a crucial part in a Neural Network's operation. The input layer receives information, forwards it to the hidden layers, that relay this information to the output layers. Basically, data flows from left to right through the nodes via the connections. Easy-peasy, right?

However, the data we get from the output layer is vastly different than the information we feed into the input layer. How does that happen and why is it desirable?

Read on to find out!
 

The "How" Of Neural Networks


TechDogs-"The "How" Of Neural Networks"-An Image Showing The Simplified Layout Of A Neural Network Node
To recap - a Neural Network is an information processing model. Information flows through it in two directions - forward and backward. While being trained, data is fed into the input layer nodes (purple), through the hidden layers (blue) to the output layer nodes (green). This standard design is called a feedforward network.

As the data travels from one node to another, it changes. This is because each connection between nodes has a unique weight (for example, weight0 and weight1 in the image) associated with it that transforms the information. Hence, the data that reaches the right-hand node is slightly different than what was transmitted from the left node. With us so far?

Now, picture a Neural Network with a few hundred hidden layers and imagine how the data would change as it passes through all the layers. Our output would be entirely different than the input - like comparing apples with oranges! Here's where the backward flow of information comes into play - backpropagation. It involves comparing the output a network generates with the output it should have yielded, i.e., the expected outcome.

When training a Neural Network, all weights are initially set to random values. Comparing the difference between expected and generated outcomes, the Neural Network learns on its own which weights to adjust and by how much, so that the generated output starts resembling the expected outcome. Backpropagation gradually reduces the difference between the generated and expected output till the model produces the desired result.

Viola! Your Neural Network is now trained! Let's understand how we can use it.
 

Where Are Neural Networks Used?


The most ground-breaking feature of Neural Networks is that once trained, they learn on their own! If you input a large enough training set, the Network will work its magic, identify the common features and then identify the same (or similar) objects in other images. For example, an object recognition application of Neural Nets will be trained using thousands of pre-labeled images of objects (cars, trees, buildings, etc.) so it can find these objects in other photos.

So, let's look at some typical applications of Neural Nets.
 

  • Speech Recognition

    Voice interfaces are being steadily integrated by businesses worldwide; however, these systems have limited vocabulary, and training them can get quite expensive. Neural Networks enable researchers to input short audio segments (no matter the language) and train the model until it accurately identifies the words in the audio. It can then be used in various speech recognition applications such as real-time language translation.

  • Character Recognition

    Training computers to recognize alphabets, symbols and digits is an age-old problem. Hence, Neural Nets have been trained to recognize characters automatically - even if they are handwritten! From reading zip codes on documents to deciphering writings in old manuscripts - Neural Nets see it all.

  • Named Entity Recognition (NER)

    This application of Neural Nets classifies entities mentioned in a text (such as Albert Einstein, Microsoft, Paris, etc.) into predefined categories like people, organizations, locations, etc. This helps identify the crucial elements in a text and sort extensive unstructured data into a more understandable format.

  • Human Face Recognition

    Another pattern recognition application is face recognition. Commonly used for biometric security purposes, a Neural Network can be trained to identify human faces accurately. The model can remember the most minute details of a person's face, so no Ethan Hunt, even a mask won't help you this time.

 

What Is The Future Of Neural Nets?

 
It is challenging to predict where this technology will go next; however, we can expect one change soon - hardware development. Neural Network research is extremely slow at the moment due to the limitations of processors. Sometimes it takes months for a Neural Network to be trained!

Having faster processors will undoubtedly enhance the effectiveness of Neural Networks and open up a world of possibilities. Training surgical robots to perform complex operations? Possible. Automating self-driving cargo trucks to identify sharp turns and bends correctly? Also, possible. Truth be told, Neural Networks are pushing the envelope of automation every day, so who knows what tomorrow might bring!


Harder, Better, Faster, Stronger!


Neural Networks have the extraordinary ability to extract meaning from complex and convoluted data for us. This has provided us with great convenience - better predictions and forecasting, faster information processing, and accurate insights from extensively large datasets. In fact, the field of Machine Learning relies heavily on Neural Nets!

As this tech advances, it will be applied to more than just financial services, forecasting, risk assessment, etc. With sophisticated hardware and better algorithms, Neural Nets will indeed become Better, Faster, Stronger. (Daft PUNk intended!)

Frequently Asked Questions

What is a Neural Network?


A Neural Network is a computational model inspired by the human brain's structure and function. It consists of interconnected nodes, or artificial neurons, arranged in layers. These networks analyze vast datasets to recognize patterns and relationships, making predictions or classifications based on the data. Neural Networks can handle complex information processing tasks, often outperforming traditional algorithms in tasks like image recognition, speech processing, and data analysis.

How do Neural Networks work?


Neural Networks process information through interconnected layers of artificial neurons. Information flows through the network in two directions: forward and backward. During training, data is fed into the input layer, passes through the hidden layers, and produces an output at the output layer. Each connection between neurons has an associated weight, which adjusts during training to minimize the difference between the network's output and the expected outcome. This adjustment process, known as backpropagation, allows the network to learn from its mistakes and improve its performance over time.

Where are Neural Networks used?


Neural Networks find applications across various fields, including speech recognition, character recognition, named entity recognition, and human face recognition. These networks excel in tasks that involve pattern recognition and classification, such as identifying spoken words, recognizing handwritten characters, or classifying entities in text. Moreover, Neural Networks are increasingly being applied in areas like image processing, natural language processing, and robotics, driving advancements in fields such as healthcare, finance, and transportation.

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