Deep Learning
Deep learning networks can be successfully applied to big data for knowledge discovery, knowledge application, and knowledge-based prediction. In other words, deep learning can be a powerful engine for producing actionable results.
The ability to learn from unlabeled or unstructured data is an enormous benefit for those interested in real-world applications.  Deep learning unlocks the treasure trove of unstructured big data for those with the imagination to use it.
            The discovery and recognition of patterns and regularities in the world around us lies at the heart of scientific and technological progress. 

            Deep learning algorithms facilitate this process understanding, modeling and forecasting the behavior of major decision variables.
Deep Learning:

         Deep learning is a technique with a growing importance, as the size of the datasets experimental sciences are facing is rapidly growing. Problems it tackles range from building a prediction function linking different observations, to classifying observations, or learning the structure in an unlabeled dataset. It has wide ranging applications in business and other fields as:

Self-driving cars
Computer vision
Speech recognition
Entertainment (Vevo)
Fake news detection
Malware detection
Fraud detection
Insurance risk and underwriting
Financial engineering
Agriculture
Manufacturing
Image resolution enhancement
Detecting developmental delay in children

Our deep learning techniques:
Tools Expertise:
R
Python
Fully Connected Neural Networks
Convolutional Neural Networks
Recurrent Neural Network
Generative Adversarial Network(GAN)
Deep Reinforcement Learning
Unsupervised Pre-trained Networks
Support-vector machines and kernel methods
Recursive Neural Networks
Artificial neural network
Active learning
Reinforcement learning
Principal Component Analysis
Max Pooling
Batch Normalization
Independent Component Analysis
Long Short-Term Memory
Transfer Learning
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Artificial Intelligence Services
AI > Decision Science