A Beginners Guide to Neural Networks

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Perceptron: The very first artificial neural network invented in 1958 by Frank Rosenblatt, an American psychologist. A renowned expert in the field of artificial intelligence. Frank’s intention of inventing ‘Perceptron’ was to model and find out how the human brain can process visual data and at the same time possess the power to recognize objects.

Meanwhile, other researchers have tried using similar artificial neural networks (ANNs) to study the human brain.

Eventually, it was then when someone realized, instead of using it to provide insights into the functionality of the human brain, why not simply use ANN for their purpose. The ability of pattern matching and learning capabilities offers them the ability to solve problems that were once challenging or impossible to solve without using a statistical method or standard computational.

The late 1980s saw real-world institutes using ANN for varied purposes.

ANN was referred to as neural networks; however, this name only belongs to the biological brain from where the model started.

Neural network: A basic introduction

According to Dr. Robert Hecht-Nielsen, an inventor of one of the first neurocomputers defines a neural network as,

“…a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.”

The simplest term one can think about is that it is also referred to as an artificial neural network (ANN). Simply put, ANNs are processing devices i.e. algorithms that are modeled after the mammalian cerebral cortex but only are smaller scales.

A large ANN is said to have thousands of processor units and the mammalian brain has billions of neurons with a corresponding height of magnitude to maintain their emergent behavior and interaction.

Like any other tool and technology, neural networks use machine learning algorithms that process complex data inputs for the computer to understand. In present times, the neural network has been applied to multiple real-world problems such as voice recognition, image recognition, finance, email filtering, medical diagnosis, and many other applications.

The neural network has three layers:

1. Input layer – most of the inputs are feed through this layer

This layer is the layer that connects with the external environment to present a pattern to the neural network. Its sole purpose is to deal with the inputs. Further on, the input is transferred to the hidden layer. Precisely, the input should provide a pattern for which the neural network is to be trained.

2. Hidden layers – there can be multiple hidden layers used for processing the inputs obtained from input layers

The hidden layer consists of neurons that already have activated functions added to it. Being an intermediate layer between input and output gives it the responsibility to extract the required features from the input data.

3. Output layer – the data once processed will be available in the output layer

The job of the output layer is to transfer information based on the design it is ordered to give. Also, the pattern given by the output layer can be easily traced back through the input layer. The neurons present in the output layer must be related to the type of work the neural network was doing.

How neural networks work?

Machine learning that utilizes neural networks does not specifically require to be programmed with rules to understand what is expected from the input. Instead, it learns from the processed labeled examples that have been fed with answers that are supplied during the training.

With the help of answer key, it learns the characteristics of the input then the correct output is set. Once the output has been set with sufficient examples, the neural network starts processing new and unseen input and in return generate successful results.

A simple explanation,

Imagine you’re teaching a network by showing it pictures of chairs and tables, represented in a manner it can comprehend, demonstrating which item is a chair and which item is a table. After a period, you show the network, a set of multiple chairs and tables likewise and you feed in an image the network hasn’t encountered previously. Do you want to give it a try? Why not just say the words chaise longue and see for yourself what happens next? Well, depending on how you’ve trained the network, it will attempt to categorize whether the item is a chair or a table, taking the reference of past experiences. Congratulations, you’ve done a great job by teaching the computer how to identify a piece of furniture.

We aren’t done yet. Interesting, isn’t it? But hey, this does not mean that a neural network can just look at an image and instantly recognize it. Looking at the example mentioned, the network isn’t viewing the pictures of the furniture. Rather they’re capturing inputs into the network, and these inputs are binary numbers. The input unit will either be switched on or switched off. For example, if you’re having five input units, you can feed in the information of five features of different chairs or tables using binary i.e. yes/no answers.

The questions could either be,

• Does the chair have a back?
• Does it have a top?
• Can you comfortably sit on it for long hours?
• Is it possible to put a lot of things on top of it?
• And does the chair have upholstery?

To which the answers for a chair would be a typical yes, no, yes, yes, no or no, no, yes, yes, no or 10110 or 00110 in binary. And for a table it would be a no, no, yes, no, yes or yes, yes, no, yes, yes – 00101 or 11011 in binary. Thus, during the learning phase, the network consists of many numbers such as 110011 or 001100.

What are the types and applications of neural networks?

Most of the things we do daily involve neural networks to help us recognize patterns to make informed decisions. Therefore, neural networks can help us in multiple ways today. Few of the common applications include:

• Operating radar scanning systems that can automatically identify enemy warship or aircraft
• Forecast the stock market or perhaps the weather report
• Help doctors detect the disease based on the symptoms they’re showing
• Apps that recognize your handwriting at the touch of your fingers on the phone

“The neural network is this kind of technology that is not an algorithm, it is a network that has weights on it, and you can adjust the weights so that it learns. You teach it through trials.”

– Howard Rheingold

 

 

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