Neural Network- Industry Use-Cases | Task-20 | Arth |

A neural network is a series of algorithms which attempt to recognize underlying relationships in a dataset through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.

The simplest version of an artificial neural network is based on Rosenblatt’s perceptron which has three layers of neurons. The first is the Input layer, which takes input values or data. The Input layer neurons are connected to a middle layer, called the Hidden layer, where all the processing actually happens. The hidden layer neurons are then connected to the final output layer. This final layer is called Output layer which gives us the output or answer to what the network has been trained to do.

Perceptrons were limited by having only a single middle “hidden” layer of neurons. Although Rosenblatt knew having more inner hidden layers would be helpful, he did not find a way to train such a network. It wasn’t until connectionists, like Geoffrey Hinton, applied the algorithm known as “backpropagation” to training networks with multiple hidden layers, that this problem was solved. Networks with many hidden layers are also known as “multi-layer perceptrons” or as “deep” neural networks, hence the term “deep” learning. How many layers and how many neurons an artificial neural network should have is known as its “architecture,” and figuring out the best one for a particular problem is currently a process of trial and error and closer to an art than a science.

  • Oil & Gas Industry

Deep learning neural networks are used to unseal insights from data that were previously hidden in order to achieve important goals such as seismic modeling, automated well planning, predicting machinery failure, and optimizing supply chains.

With deep learning, well Operators are able to visualize and analyze massive volumes of production and sensor data such as flow rates, pump pressures, and temperatures. This capability affords better insights into critical issues such as predicting which pieces of equipment might fail and how these failures could affect systems on a wider basis.

Deep learning algorithms allow oil and gas companies to determine the best way to optimize their operations as conditions continue to change. They can use deep learning to train models to predict and improve the efficiency, reliability, and safety of expensive drilling and production operations.

  • Construction Industry

Construction companies use deep learning to testing out sequences of installing pipe laying concrete to find the optimal sequence.

Each project is unique, which means there’s essentially no availability of training data from past projects that can be used for training algorithms. In order to get over this hurdle, Reinforcement Learning is used where simulations essentially become the training data set.

  • Financial Services Industry

There are many opportunities for applying deep learning technology in the financial services industry. One important task that deep learning can perform is e-discovery. For example, large investment houses like JPMorgan Chase are using deep learning based text analytics for insider trading detection and government regulatory compliance. Hedge funds use text analytics to drill down into massive document repositories for obtaining insights into future investment performance and market sentiment. The use case for deep learning based text analytics revolves around its ability to parse massive amounts of text data to perform analytics or yield aggregations.

  • Cyber-Security

Deep learning also has a number of use cases in the cyber-security space. One of the advantages that deep learning has over other approaches is accuracy. In many cases, the improvement approaches a 99.9% detection rate. The high risk and cost associated with not detecting a security threat make the expense related with deep learning justified.

Deep learning can play a number of important roles within a cybersecurity strategy. Use cases include automating intrusion detection with an exceptional discovery rate. Deep learning also performs well with malware, as well as malicious URL and code detection. Deep learning for cybersecurity is a motivating blend of practical applications along with untapped potential. With proper vetting, it’s well worth the effort to ensure the time and investment required for implementing a solution that yields the anticipated gains.

  • Social Media

Deep learning’s power can also be seen with how it’s being used in social media technology. Let’s take Pinterest for example, which includes a visual search tool that lets you zoom in on a specific object in a “Pin” (or pinned image) and discover visually similar objects, colors, patterns and more. The company’s engineering team used deep learning to teach their system how to recognize image features using a richly annotated data set of billions of Pins curated by Pinterest users. The features can then be used to compute a similarity score between any two images and identify the best matches.

Conclusion

Deep learning is rapidly transforming many industries including healthcare, energy, fintech, transportation, and many others, to rethink traditional business processes with digital intelligence. Early adopter industries have witnessed a profound effect on the workplace and great potential in terms of developing deep learning applications, which can be used for yielding forecasts, detecting fraud, attracting new customers, and so much more. The opportunities and capabilities are substantial and that’s why many enterprises are investing in deep learning for building out their existing applications as well as developing new solutions.

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