Industry Use cases of Neural Networks

Introduction to Neural Networks:

Neural networks represent deep learning using artificial intelligence. Certain application scenarios are too heavy or out of scope for traditional machine learning algorithms to handle. As they are commonly known, Neural Network pitches in such scenarios and fills the gap.

Artificial neural networks are inspired from the biological neurons within the human body which activate under certain circumstances resulting in a related action performed by the body in response. Artificial neural nets consist of various layers of interconnected artificial neurons powered by activation functions which help in switching them ON/OFF. Like traditional machine algorithms, here too, there are certain values that neural nets learn in the training phase.

Understanding Neural Network in depth:

Briefly, each neuron receives a multiplied version of inputs and random weights which is then added with static bias value (unique to each neuron layer), this is then passed to an appropriate activation function which decides the final value to be given out of the neuron. There are various activation functions available as per the nature of input values. Once the output is generated from the final neural net layer, loss function (input vs output)is calculated and backpropagation is performed where the weights are adjusted to make the loss minimum. Finding optimal values of weights is what the overall operation is focusing around.

Weights are numeric values which are multiplied with inputs. In backpropagation, they are modified to reduce the loss. In simple words, weights are machine learnt values from Neural Networks. They self-adjust depending on the difference between predicted outputs vs training inputs.

Activation Function is a mathematical formula which helps the neuron to switch ON/OFF.

• Input layer represents dimensions of the input vector.
• Hidden layer represents the intermediary nodes that divide the input space into regions with (soft) boundaries. It takes in a set of weighted input and produces output through an activation function.
• Output layer represents the output of the neural network.

Types of Neural Networks

1) Recurrent Neural Network (RNN)

In this network, the output of a layer is saved and transferred back to the input. This way, the nodes of a particular layer remember some information about the past steps. The combination of the input layer is the product of the sum of weights and features. The recurrent neural network process begins in the hidden layers.

Here, each node remembers some of the information of its antecedent step. The model retains some information from each iteration, which it can use later. The system self-learns when its outcome is wrong. It then uses that information to increase the accuracy of its prediction in back-propagation. The most popular application of RNN is in text-to-speech technology. 

2) Convolutional Neural Network (CNN)

This network consists of one or multiple convolutional layers. The convolutional layer present in this network applies a convolutional function on the input before transferring it to the next layer. Due to this, the network has fewer parameters, but it becomes more profound. CNNs are widely used in natural language processing and image recognition. 

3) Radial Basis Function Neural Network (RBFNN)

This neural network uses a radial basis function. This function considers the distance of a point from the center. These networks consist of two layers. The hidden layer combines the features with the radial basis function and transfers the output to the next layer. 

The next layer performs the same while using the output of the previous layer. The radial basis function neural networks are used in power systems. 

4) Feedforward Neural Network (FNN)

This is the purest form of an artificial neural network. In this network, data moves in one direction, i.e., from the input layer to the output layer. In this network, the output layer receives the sum of the products of the inputs and their weights. There’s no back-propagation in this neural network. These networks could have many or zero hidden layers. These are easier to maintain and find application in face recognition. 

5) Modular Neural Network

This network possesses several networks that function independently. They all perform specific tasks, but they do not interact with each other during the computation process.

This way, a modular neural network can perform a highly complex task with much higher efficiency. These networks are more challenging to maintain in comparison to simpler networks (such as FNN), but they also deliver faster results for complex tasks. 

Now,Let's discuss some of it's use cases.

Applications of neural networks in the pharmaceutical industry

The most obvious application is in the field of disease identification and diagnosis.It was reported in 2015 that in America 800 possible cancer treatments were in the trial.With so much data being produced, Artificial Neural Networks are being used to help scientists efficiently analyse and interpret it.The IBM Watson Genomics is one example of smart solutions being used to process large amounts of data.IBM Watson Genomics is improving precision medicine by integrating genomic tumour sequencing with cognitive computing.With a similar aim in mind, Google has developed DeepMind Health.Working alongside a number of medical specialists such as Moorfields Eye Hospital, the company is looking to develop a cure for macular degeneration.

Neural Networks in the Retail Sector

As we have noted, Artificial Neural Networks are versatile systems, capable of dealing reliably with a number of different factors.This ability to handle a number of variables makes Artificial Neural Networks an ideal choice for the retail sector.For instance, Artificial Neural Networks are, when given the right information, able to make accurate forecasts.These forecasts are often more accurate than those made in the traditional manner, by analysing statistics.This can allow accurate sales forecasts to be generated.In turn, this information allows your businesses to purchase the right amount of stock.This reduces the chances of selling out of certain items.It also reduces the risk of valuable warehouse space being taken up by products you are unable to sell.

Applications to Encourage Repeat Custom

As well as monitoring and suggesting purchases, Artificial Neural Network systems also allow you to analyse the time between purchases.This application is most useful when monitoring individual customer habits.For example, a customer may buy new ink cartridges every 2 months.Systems powered by Artificial Neural Networks can identify and monitor this repeat custom.You can then contact your customer and remind them to buy when the time to purchase the product approaches.This friendly reminder increases the chances of the customer returning to your store to make their purchase.Retailers that offer loyalty schemes are already taking advantage of this.Beauty brand Sephora’s Beauty Insider program records every purchase a customer makes.It also records how frequently these purchases are made.This information allows the company to predict when a customer’s products may be running low.At this point the company sends a “restock your stash” email, prompting the customer to make a repeat purchase.This information can also be used to develop a personalised marketing approach offering incentives or discounts.

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