Deep Learning

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.

Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks 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 systems 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 class of machine learning algorithms that:

·      Use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.

·      Learn in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners.

·      Learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.

Applications in Businesses:

Deep Learning algorithms are becoming more widely used in every industry sector from online retail to photography; some use cases are more popular and have attracted extra attention of global media than others. Some widely publicized Deep Learning applications include:

·      Speech recognition used by Amazon Alexa, Google, Apple Siri, or Microsoft Cortana.

·      Image recognition used for analyzing documents and pictures residing on large databases.

·      Natural Language Processing (NLP) used for negative sampling, sentiment analysis, machine translation, or contextual entity linking.

·      Automated drug discovery and toxicology used for drug design and development work, as well as for predictive diagnosis of diseases.

·      CRM activities used for automated marketing practices.

·      Recommendation engines used in a variety of applications.

·      Predictions in gene ontology and gene-function relationships.

·      Health predictions based on data collected from wearables and EMRs.