अर्थ Program Task — 20

ARTH — Task 20 👨🏻‍💻

Task Description📄

✍🏻 Research for industry use cases of Neural Networks and create a blog, Article or Video elaborating how it works.

Neural Networks

Neural networks are a set of algorithms, they are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data.

Let’s take a moment to consider the human brain. Made up of a network of neurons, the brain is a very complex structure. It’s capable of quickly assessing and understanding the context of numerous different situations. Computers struggle to react to situations in a similar way. Artificial Neural Networks are a way of overcoming this limitation.

First developed in the 1940s Artificial Neural Networks attempt to simulate the way the brain operates. Sometimes called perceptrons, an Artificial Neural Network is a hardware or software system. Some networks are a combination of the two. Consisting of a network of layers this system is patterned to replicate the way the neurons in the brain operate. The network comprises an input layer, where data is entered, and an output layer. The output layer is where processed information is presented. Connecting the two is a hidden layer or layers. The hidden layers consist of units that transform input data into useful information for the output layer to present. In addition to replicating the human decision making progress Artificial Neural Networks allow computers to learn.

Their structure also allows ANN’s to reliably and quickly identify patterns that are too complex for humans to identify. Artificial Neural Networks also allow us to classify and cluster large amounts of data quickly.

How Artificial Neural Networks Work?

As we have seen Artificial Neural Networks are made up of a number of different layers. Each layer houses artificial neurons called units. These artificial neurons allow the layers to process, categorize, and sort information. Alongside the layers are processing nodes. Each node has its own specific piece of knowledge. This knowledge includes the rules that the system was originally programmed with. It also includes any rules the system has learned for itself. This makeup allows the network to learn and react to both structured and unstructured information and data sets.

Almost all artificial neural networks are fully connected throughout these layers. Each connection is weighted. The heavier the weight, or the higher the number, the greater the influence that the unit has on another unit.

The first layer is the input layer. This takes on the information in various forms. This information then progresses through the hidden layers where it is analysed and processed. By processing data in this way, the network learns more and more about the information.

Eventually, the data reaches the end of the network, the output layer. Here the network works out how to respond to the input data. This response is based on the information it has learned throughout the process. Here the processing nodes allow the information to be presented in a useful way.

Educating Artificial Neural Networks

For artificial neural networks to learn they require a mass of information. This information is known as a training set. If we wanted to teach our ANN to learn how to recognise a cat your training set would consist of thousands of images of a cat. These images would all be tagged “cat”. Once this information has been inputted and analysed the network is considered trained.

From now on it will try to classify any future data based on what it thinks it is seeing. So if you present it with a new image of a cat, it will identify the creature. As a check, during the training period, the system’s output is matched against the description of the data it’s analysing. If the information is the same, the learning process is validated. If the information is different backpropagation is used to adjust the learning process.

Backpropagation involves working back through the layers, adjusting the set mathematical equations and parameters. These adjustments are made until the output data presents the desired result. This process, deep learning, is what makes the network adaptive. The network is able to learn and adapt as more information is processed.

Artificial Neural Networks Uses

Artificial Neural Networks can be used in a number of ways. They can classify information, cluster data, or predict outcomes. ANN’s can be used for a range of tasks. These include analyzing data, transcribing speech into text, powering facial recognition software, or predicting the weather.

There are many types of Artificial Neural Network. Each has its own specific use. Depending on the task it is required to process the ANN can be simple or very complex. The most basic type of Artificial Neural Network is a feedforward neural network. This is a basic system where information can travel in only one direction, from input to output.

Industries Use Cases

Marketing Strategies

By adopting Artificial Neural Networks businesses are able to optimise their marketing strategy. Systems powered by Artificial Neural Networks all capable of processing masses of information. This includes customers personal details, shopping patterns as well as any other information relevant to our business.

Once processed this information can be sorted and presented in a useful and accessible way. This is generally known as market segmentation. To put it another way segmentation of customers allows businesses to target their marketing strategies. Businesses can identify and target customers most likely to purchase a specific service or produce.

This focusing of marketing campaigns means that time and expense isn’t wasted advertising to customers who are unlikely to engage. This application of Artificial Neural Networks can save businesses both time and money. It can also help to increase profits.

The flexibility of Artificial Neural Networks means that their marketing applications can be implemented by most businesses. Artificial Neural Networks can segment customers on multiple characteristics. These characteristics can be as diverse as location, age, economic status, purchasing patterns and anything else relevant to your business.

One company making the most of this flexibility is cosmetics brand Sephora. The email marketing campaign is tailored to the interests of each customer on the mailing list. This allows them to offer a seamless, targeted marketing campaign. This approach means that at a time when many companies are struggling Sephora is flourishing.

Developing Targeted Marketing Campaigns

Through unsupervised learning, Artificial Neural Networks are able to identify customers with a similar characteristic. This allows businesses to group together customers with similarities, such as economic status or preferring vinyl records to downloaded music.

Supervised learning systems allow Artificial Neural Networks to set out a clear aim for your marketing strategy. Like unsupervised systems, they can also segment customers into similar groupings. However supervised learning systems are also able to match customer groupings to the products they are most likely to buy.

This application of technology can increase profits by driving sales. Starbucks has used Artificial Neural Networks and targeted marketing to keep customers engaged with their app. The company has integrated its rewards system location and purchase history on their app. This allows them to offer an incredibly personalised experience, helping to increase revenue by $2.56 billion.

Thank You for reading !!!