Thought

Leverage Deep Learning to make sense of and benefit from the explosion in the amount of training data; the use of accelerators such as graphics processing units (GPUs); and the advancement in the training algorithms and neural network architectures to infuse AI into your business to drive innovation. Deep learning involves building and training a “neural network,” a machine learning model inspired by the human brain. Build, train and deploy models at scale and once a neural network is trained on a dataset, it can be used for a variety of recognition tasks.

Drivers & Benefits

Creating New Features

One of the main benefits of deep learning over various machine learning algorithms is its ability to generate new features from limited series of features located in the training dataset. Therefore, deep learning algorithms can create new tasks to solve current ones. What does it mean for data scientists working in technological startups? Since deep learning can create features without a human intervention, data scientists can save much time on working with big data and relying on this technology. It allows them to use more complex sets of features in comparison with traditional machine learning software.

Advanced Analysis

Due to its improved data processing models, deep learning generates actionable results when solving data science tasks. While machine learning works only with labeled data, deep learning supports unsupervised learning techniques that allow the system become smarter on its own. The capacity to determine the most important features allows deep learning to efficiently provide data scientists with concise and reliable analysis results.

Applications

Deep learning

Deep learning can help an insurance company determine how much a car has been damaged after an accident. How? An image of the damaged car can be included within a dataset trained at detecting not only the car make and model, but also where the car has sustained damage. Once the deep learning AI system recognizes the car, it compares the image of the damaged car to its dataset —and then classifies that damaged car as, for example, missing a bumper.

Banking

Inefficient paper-based credit scoring at a major French bank delayed loan applications, requiring customers to visit their branch twice for a decision, and raising the risk that they would choose another bank. The bank created a hybrid-cloud credit-scoring app that uses advanced machine learning to rapidly and accurately determine each customer’s creditworthiness.

Video Surveillance

What if you could use deep learning to investigate hours of video surveillance footings to identify when truck drivers cheat and use the company card to fill up private canisters with diesel? Diesel to be used at home, in private cars or sold on the side. With Power AI Vision it is quite easy to train a DL-system to detect specific objects in images and video streams and raise a flag. And it GOES FAST, which is absolutely critical to the Danish end-user customer. Solution developed by an IBM Business Partner.

Financial Institutions

What if you are one of the largest financial institutions, serving your customers with a multitude of services and now wants to offer Fraud Surveillance & Detection (in trading e-mail communication) as a PaaS-service?

Fashion

What if you are one of the largest FASHION retailers in the world and want to make sure, you are successful whenever you launch a new brand or a new fashion line, no matter where in the world your stores are located? Knowing customer behavior coupled with market data got a European company starting using ML and DL years ago.