Industry Use-cases Of Deep learning And Neural Networks

In this article, we are going to discuss about the industry use cases of Deep learning And Neural Networks.

💥Deep Learning💥

The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires thought to figure out is a problem deep learning can learn to solve.

The amount of data we generate every day is staggering, generating very rapidly and it’s the resource that makes deep learning possible. Since deep-learning algorithms require a ton of data to learn from, this increase in data creation is one reason that deep learning capabilities have grown in recent years. In addition to more data creation, deep learning algorithms benefit from the stronger computing power that’s available today as well as the proliferation of Artificial Intelligence (AI) as a Service. AI as a Service has given smaller organizations access to artificial intelligence technology and specifically the AI algorithms required for deep learning without a large initial investment.

Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. The more deep learning algorithms learn, the better they perform.

  • Virtual assistants
  • Translations
  • Vision for driverless delivery trucks, drones and autonomous cars
  • Chatbots and service bots
  • Image colorization
  • Facial recognition
  • Medicine and pharmaceuticals.
  • Personalized shopping and entertainment

💥Neural Network/Artificial neural networks💥

A branch of machine learning, neural networks (NN), also known as artificial neural networks (ANN), are computational models essentially algorithms. Neural networks have a unique ability to extract meaning from imprecise or complex data to find patterns and detect trends that are too convoluted for the human brain or for other computer techniques. Neural networks have provided us with greater convenience in numerous ways, including through ridesharing apps, Gmail smart sorting, and suggestions on Amazon

An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it. The signal at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.

How a Single Neuron works?🤔

In this section, we will explore the working of a single neuron with easy examples. The idea is to give you some intuition on how a neuron compute outputs using the inputs. A typical neuron looks like:

The different components are:

  1. x1, x2,…, xN: Inputs to the neuron. These can either be the actual observations from input layer or an intermediate value from one of the hidden layers.
  2. x0: Bias unit. This is a constant value added to the input of the activation function. It works similar to an intercept term and typically has +1 value.
  3. w0,w1, w2,…,wN: Weights on each input. Note that even bias unit has a weight.
  4. a: Output of the neuron which is calculated as:

Here f is known an activation function. This makes a Neural Network extremely flexible and imparts the capability to estimate complex non-linear relationships in data. It can be a gaussian function, logistic function, hyperbolic function or even a linear function in simple cases.

Lets implement 3 fundamental functions — OR, AND, NOT using Neural Networks. This will help us understand how they work. You can assume these to be like a classification problem where we’ll predict the output (0 or 1) for different combination of inputs.

We will model these like linear classifiers with the following activation function:

Example 1: AND

The AND function can be implemented as:

The output of this neuron is:

a = f( -1.5 + x1 + x2 )

The truth table for this implementation is:

Here we can see that the AND function is successfully implemented. Column ‘a’ complies with ‘X1 AND X2’. Note that here the bias unit weight is -1.5. But it’s not a fixed value. Intuitively, we can understand it as anything which makes the total value positive only when both x1 and x2 are positive. So any value between (-1,-2) would work.

Example 2: OR

The OR function can be implemented as:

The output of this neuron is:

a = f( -0.5 + x1 + x2 )

The truth table for this implementation is:

Column ‘a’ complies with ‘X1 OR X2’. We can see that, just by changing the bias unit weight, we can implement an OR function. This is very similar to the one above. Intuitively, you can understand that here, the bias unit is such that the weighted sum will be positive if any of x1 or x2 becomes positive.

Example 3: NOT

Just like the previous cases, the NOT function can be implemented as:

The output of this neuron is:

a = f( 1–2*x1 )

The truth table for this implementation is:

Again, the compliance with desired value proves functionality. I hope with these examples, you’re getting some intuition into how a neuron inside a Neural Network works. Here I have used a very simple activation function.

Note: Generally a logistic function will be used in place of what I used here because it is differentiable and makes determination of a gradient possible. There’s just 1 catch. And, that is, it outputs floating value and not exactly 0 or 1.

What are Artificial Neural Networks Used for?🤔

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.

💥Real-World and Industry Applications of Neural Networks💥

The companies and government agencies that have begun enlisting the automation software run the gamut. They include General Motors, BMW, General Electric, Unilever, MasterCard, Manpower, FedEx, Cisco, Google, the Defense Department, and NASA. We’re just seeing the beginning of neural network/AI applications changing the way our world works.

Engineering Applications of Neural Networks

  • Engineering is where neural network applications are essential, particularly in the “high assurance systems that have emerged in various fields, including flight control, chemical engineering, power plants, automotive control, medical systems, and other systems that require autonomy.”
  • Aerospace: Aircraft component fault detectors and simulations, aircraft control systems, high-performance auto-piloting, and flight path simulation.
  • Automotive: Improved guidance systems, development of power trains, virtual sensors, and warranty activity analyzers
  • Electronics: Chip failure analysis, circuit chip layouts, machine vision, non-linear modeling, prediction of the code sequence, process control, and voice synthesis
  • Manufacturing: Chemical product design analysis, dynamic modeling of chemical process systems, process control, process and machine diagnosis, product design and analysis, paper quality prediction, project bidding, planning and management, quality analysis of computer chips, visual quality inspection systems, and welding quality analysis
  • Mechanics: Condition monitoring, systems modeling, and control
  • Robotics: Forklift robots, manipulator controllers, trajectory control, and vision systems
  • Telecommunications: ATM network control, automated information services, customer payment processing systems, data compression, equalizers, fault management, handwriting recognition, network design, management, routing and control, network monitoring, real-time translation of spoken language, and pattern recognition (faces, objects, fingerprints, semantic parsing, spell check, signal processing, and speech recognition)

✨Improving Search Engine Functionality✨

During 2015 Google I/O keynote address in San Francisco, Google revealed they were working on improving their search engine.

These improvements are powered by a 30 layer deep Artificial Neural Network.

This depth of layers, Google believes, allows the search engine to process complicated searches such as shapes and colours.

Using an Artificial Neural Network allows the system to constantly learn and improve.

This allows Google to constantly improve its search engine.

Within a few months, Google was already noticing improvements in search results.

The company reported that its error rate had dropped from 23% down to just 8%.

Google’s application shows that neural networks can help to improve search engine functionality.

Similar Artificial Neural Networks can be applied to the search feature on many e-commerce websites.

This means that many companies can improve their website search engine functionality.

This allows customers with only a vague idea of what they want to easily find the perfect item.

Amazon has reported sales increases of 29% following improvements to its recommendation systems.

How Companies Are Using Neural Networks?🤔

Companies are using neural networks in various ways, depending on their business model. LinkedIn for instance, uses neural networks along with linear text classifiers to detect spam or abusive content in its feeds when it is created. It also used neural networks to help understand all kinds of content shared on LinkedIn — ranging from news articles to jobs to online classes — so that ,it can build better recommendation and search products for members and customers⚡.

MASTERCARD — Fraud Detection Applications🤔

As technology advances, and more importance is placed on online transactions, fraudsters are also becoming more sophisticated. Luckily Artificial Neural Networks can help to keep us, and our finances, safe.

Deep learning and Artificial Neural Networks applications are powering systems capable of detecting all forms of financial fraud.

For example, this application can identify unusual activity, such as transactions occurring outside the established time frame. Visa has used smart solutions to cut credit card fraud by two thirds. Their sophisticated anti-fraud detection systems are working towards biometric solutions. However the company also analysis information such as payment method, time, location, item purchased, and the amount spent.

Even a small deviation from the norm in any of these categories can highlight a potential fraud case. Within seconds smart solutions allow Visa to look at over 500 data elements to determine if a transaction is suspicious. Similarly, it can be embarrassing when our card is declined by a retailer. Especially if our account is in credit.

MasterCard is employing solutions powered by Artificial Neural Networks to reduce the chances of this happening🔥. Currently, MasterCard has halved the chances of these errors from occurring.

FACEBOOK — Facial Recognition Software Applications🤔

Technology companies have long been working toward developing reliable facial recognition software.

One company leading the way is Facebook. For a number of years now they have been using the facial recognition technology to auto-tag uploaded photographs. They have also developed “DeepFace”🔥.

DeepFace is a form of facial recognition software-driven by Artificial Neural Networks⚡. It is capable of mapping 3D facial features. Once the mapping is complete the software turns the information into a flat model. The information is then filtered, highlighting distinctive facial elements. To be able to do this DeepFace implements 120 million parameters. This technology hasn’t just emerged overnight. DeepFace has been trained with a pool of 4.4 million tagged faces. These images were taken from 4,000 different Facebook accounts.

DeepFace : Facial recognition software of Facebook , powered by neural networks uses a 3-D model to rotate faces, virtually, so that they face the camera. Image (a) shows the original image, and (g) shows the final corrected version.

During the training process, tests were carried out presenting the system with side-by-side images. The system was then asked to identify if the images are of the same person. In these tests, DeepFace returned an accuracy rating of 97.25%. Human participants taking the same test scored, on average, 97.5%. Facebook has also taken its software to computing and technology conferences.

This is done with the purpose of allowing academics and researchers to assess and inspect the technology. With all this work, it’s little wonder that DeepFace may be the most accurate facial technology software yet developed⚡.


Artificial neural networks have become an accepted information analysis technology in a variety of disciplines. This has resulted in a variety of commercial applications (in both products and services) of neural network technology. The applications that neural networks have been put to and the potential possibilities that exist in a variety of civil and military sectors are tremendous.

Given below are domains of commercial applications of neural network technology:-

  • Business — Marketing & Real Estate
  • Document & Form Processing — Machine printed character recognition , Graphics recognition ,Hand printed character recognition & Cursive handwritten character recognition
  • Finance Industry — Market trading ,Fraud detection & Credit rating
  • Food lndustry — Odour/aroma analysis, Product development & Quality assurance
  • Energy Industry — Electrical load forecasting ,Hydroelectric dam operation & Natural gas
  • Manufacturing — Process control & Quality control
  • Medical & Health Care Industry — Image analysis, Drug development & Resource allocation
  • Science & Engineering — Chemical engineering ,Electrical engineering & Weather forecasting.

Thanks For Reading!!



Technical Volunteer at ARTH-The School of Technologies || Cloud Enthusiast || Ansible ||Flutter || Hybrid Multi Cloud || DevOps || Terraform

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Uditanshu pandey

Technical Volunteer at ARTH-The School of Technologies || Cloud Enthusiast || Ansible ||Flutter || Hybrid Multi Cloud || DevOps || Terraform