DEEP LEARNING

Deep learning and machine learning have a huge role to play. With data volumes already overwhelming businesses and yet constantly increasing, we’re going to see more organizations looking to algorithms to do the heavy lifting and unearth actionable insights. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural network have been applied to fields including Computer Vision, Speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous system yet have various differences from the structural and functional properties of biological brains [especially human brains], which make them incompatible with neuroscience evidences

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing.

Speech Recognition

Both the business and academic worlds have embraced deep learning for speech recognition. Xbox, Skype, Google Now and Apple’s Siri?, to name a few, are already employing deep learning technologies in their systems to recognize human speech and voice patterns.

Image Recognition

Image Recognition

One practical application of image recognition is automatic image captioning and scene description. This could be crucial in law enforcement investigations for identifying criminal activity in thousands of photos submitted by bystanders in a crowded area where a crime has occurred. Self-driving cars will also benefit from image recognition through the use of 360-degree camera technology.

APPLICATION AND OPPORTUNITIES

A lot of computational power is needed to solve deep learning problems because of the iterative nature of deep learning algorithms, their complexity as the number of layers increase, and the large volumes of data needed to train the networks.

The dynamic nature of deep learning methods — their ability to continuously improve and adapt to changes in the underlying information pattern — presents a great opportunity to introduce more dynamic behavior into analytics.

Greater personalization of customer analytics is one possibility. Another great opportunity is to improve accuracy and performance in applications where neural networks have been used for a long time. Through better algorithms and more computing power, we can add greater depth.While the current market focus of deep learning techniques is in applications of cognitive computing, there is also great potential in more traditional analytics applications, for example, time series analysis.

Another opportunity is to simply be more efficient and streamlined in existing analytical operations. Recently, SAS experimented with deep neural networks in speech-to-text transcription problems. Compared to the standard techniques, the word-error-rate decreased by more than 10 percent when deep neural networks were applied. They also eliminated about 10 steps of data preprocessing, feature engineering and modeling. The impressive performance gains and the time savings when compared to feature engineering signify a paradigm shift.