Industrial Use Cases Of Neural Network
A warm welcome and lots of good wishes on becoming part of this article in this article we know about neural networks and use case o these neural networks in a scenario of industrial companies.
What Is Neural Network?
In the past 10 years, the best-performing artificial-intelligence systems — such as the speech recognizers on smartphones or Google’s latest automatic translator — have resulted from a technique called “deep learning.”
Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department.
“A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems.”
In this above image you can see that in the neural network process, 1 step is input layer which take input as a form of data and in 2 step this data is processed by the hidden layer neurons and after collect a meaning full result from this middle layer then this result is forward to the output layer.
How does a neural network learn things?
Information flows through a neural network in two ways. When it’s learning (being trained) or operating normally (after being trained), patterns of information are fed into the network via the input units, which trigger the layers of hidden units, and these in turn arrive at the output units. This common design is called a feedforward network. Not all units “fire” all the time. Each unit receives inputs from the units to its left, and the inputs are multiplied by the weights of the connections they travel along. Every unit adds up all the inputs it receives in this way and (in the simplest type of network) if the sum is more than a certain threshold value, the unit “fires” and triggers the units it’s connected to (those on its right).
Here I Relate Real World Example To The Neural Network
For a neural network to learn, there has to be an element of feedback involved — just as children learn by being told what they’re doing right or wrong. In fact, we all use feedback, all the time. Think back to when you first learned to play a game like ten-pin bowling. As you picked up the heavy ball and rolled it down the alley, your brain watched how quickly the ball moved and the line it followed, and noted how close you came to knocking down the skittles. Next time it was your turn, you remembered what you’d done wrong before, modified your movements accordingly, and hopefully threw the ball a bit better. So you used feedback to compare the outcome you wanted with what actually happened, figured out the difference between the two, and used that to change what you did next time (“I need to throw it harder,” “I need to roll slightly more to the left,” “I need to let go later,” and so on). The bigger the difference between the intended and actual outcome, the more radically you would have altered your moves.
Neural networks learn things in exactly the same way, typically by a feedback process called backpropagation (sometimes abbreviated as “backprop”). This involves comparing the output a network produces with the output it was meant to produce, and using the difference between them to modify the weights of the connections between the units in the network, working from the output units through the hidden units to the input units — going backward, in other words. In time, backpropagation causes the network to learn, reducing the difference between actual and intended output to the point where the two exactly coincide, so the network figures things out exactly as it should.
How does it work in practice?
Once the network has been trained with enough learning examples, it reaches a point where you can present it with an entirely new set of inputs it’s never seen before and see how it responds. For example, suppose you’ve been teaching a network by showing it lots of pictures of chairs and tables, represented in some appropriate way it can understand, and telling it whether each one is a chair or a table. After showing it, let’s say, 25 different chairs and 25 different tables, you feed it a picture of some new design it’s not encountered before — let’s say a chaise longue — and see what happens. Depending on how you’ve trained it, it’ll attempt to categorize the new example as either a chair or a table, generalizing on the basis of its past experience — just like a human. Hey presto, you’ve taught a computer how to recognize furniture!
That doesn’t mean to say a neural network can just “look” at pieces of furniture and instantly respond to them in meaningful ways; it’s not behaving like a person. Consider the example we’ve just given: the network is not actually looking at pieces of furniture. The inputs to a network are essentially binary numbers: each input unit is either switched on or switched off. So if you had five input units, you could feed in information about five different characteristics of different chairs using binary (yes/no) answers. The questions might be 1) Does it have a back? 2) Does it have a top? 3) Does it have soft upholstery? 4) Can you sit on it comfortably for long periods of time? 5) Can you put lots of things on top of it? A typical chair would then present as Yes, No, Yes, Yes, No or 10110 in binary, while a typical table might be No, Yes, No, No, Yes or 01001. So, during the learning phase, the network is simply looking at lots of numbers like 10110 and 01001 and learning that some mean chair (which might be an output of 1) while others mean table (an output of 0).
Application OF Neural Network
There are lots of applications for neural networks in security, too. Suppose you’re running a bank with many thousands of credit-card transactions passing through your computer system every single minute. You need a quick automated way of identifying any transactions that might be fraudulent — and that’s something for which a neural network is perfectly suited. Your inputs would be things like 1) Is the cardholder actually present? 2) Has a valid PIN number been used? 3) Have five or more transactions been presented with this card in the last 10 minutes? 4) Is the card being used in a different country from which it’s registered? — and so on. With enough clues, a neural network can flag up any transactions that look suspicious, allowing a human operator to investigate them more closely. In a very similar way, a bank could use a neural network to help it decide whether to give loans to people on the basis of their past credit history, current earnings, and employment record.
5 Industries Which Rely Heavily on Artificial Intelligence and Machine Learning
1. Transportation
If you think self-driving cars are products of a distant future, smart cars have already made their way to the markets. Back in 2015, the implementation of AI-driven systems in cars and vehicles were just 8% but by 2025, the rates are expected to jump to 109%. Connected cars are the in-thing in the automobile industry right now, where predictive mechanisms accurately tel l drivers the probable malfunctioning of spare parts, routes and driving directions, emergency and disaster prevention protocols and more. Gartner predicted that connected cars with embedded wireless connectivity and networks would be the benchmarks for cars by 2020. This is also turning slowly turning into a reality with the prototypes of autonomous cars hitting the roads.
2. Health Care
Artificial intelligence is already arriving as a game changer in the healthcare sector. IBM’s cognitive supercomputer was able to quickly diagnose the presence of a rare type of leukaemia in a patient that even doctors could not after months of study. There are algorithms and systems that aiding in detection and treatment of chronic ailments and with electronic health records in place, artificial intelligence and machine learning systems are only making personalized healthcare a reality today. Also, predictive healthcare is slowly gaining momentum as well.
3. Finance
Finance has always needed one of the most precise forms of computing systems in place for a myriad of its purposes. As far as AI and ML are concerned, the finance sector would rely heavily on the systems powered by these technologies to detect fraudulent transactions and pave way for a safer and more secure online transaction. It can also predict the rise and fall of stocks values in the market and help financial advisers with ideal investment plans.
4.Manufacturing Industries
The manufacturing industry has tons of aspects that AI-based bots or systems could fix. From robot-driven assembly lines to intelligent systems that can predict the malfunctioning of machinery, AI would become inevitable for the manufacturing industries. It could also remove employees with redundant skill sets and engage them in meaningful works. AI-based bots or machines would also assist in solving supply-chain concerns over a wide geographical location, minimizing the shipping and delivery timing of online products.
5.Advertising
Instead of spending thousands of dollars on a campaign to test if it would be effective a set pool of target audience, AI-powered systems would efficiently simulate the campaign with past data in hand and deliver precise results. This would be a game changer in the marketing realm as brands and businesses would have a sure shot avenue to place their money in. Reaching out to potential customers, generating leads and converting them to sales, identifying the market share of a new product before launch and competition research could all become easier with smart sentiment analysis tools and techniques.
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