Deep learning (DL) is an area in machine learning which has been recently reinvented. It has already led to computational breakthroughs in fields, such as object recognition in computer vision and language modeling in NLP. With DL we can model high level data abstractions by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations. Nowadays, it is considered to be a cutting edge technology and is employed in a variety of applications, while producing state-of-the-art results, and either outperforming traditional, “shallow” approaches, or yielding similar results without any feature-engineering “hand-crafting” effort.
Solving the world’s toughest problems using the state-of-the-art deep learning technology.
In our team we have rich experience and expertise in deep learning. We develop solutions in classification, regression, detection, recognition and forecasting, while applying architectures such as deep neural networks (DNNs), convolutional deep neural networks (CNNs), deep belief networks (DBNs), recurrent neural networks (RNNs), in real-world industrial problems such as hyper personalization and recommendation systems, demand forecasting, fraud detection, stock forecasting, credit risk management to name but a few.