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. 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 “hand-crafting” effort.
In our team we employ DL in problems such as classification, recognition, detection, forecasting and optimization by applying architectures such as deep neural networks (DNNs), convolutional deep neural networks (CNNs), deep belief networks (DBNs), recurrent neural networks (RNNs) and long short term memory (LSTM), to problems in computer vision, automatic speech recognition, natural language processing, stock forecasting and recommendation systems, to name but a few. Please, check out our use cases