Lucrative applications

In the past few years, the number of researchers and engineers in deep learning has grown at an exponential rate. Deep learning breaks new ground in almost every domain it touches using novel neural networks architectures and advanced machine learning frameworks. With significant hardware and algorithmic developments, deep learning has revolutionized the industry and has been highly successful in tackling many real-world AI and data mining problems.

We have seen an explosion in new and lucrative applications using deep learning frameworks in areas as diverse as image recognition, image search, object detection, computer vision, optical character recognition, video parsing, face recognition, pose estimation (Cao and others, Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields, 2016), speech recognition, spam detection, text to speech or image caption, translation, natural language processing, chatbots, targeted online advertising serving, click-through optimization, robotics, computer vision, energy optimization, medicine, art, music, physics, autonomous car driving, data mining of biological data, bioinformatics (protein sequence prediction, phylogenetic inferences, multiple sequence alignment) big data analytics, semantic indexing, sentiment analysis, web search/information retrieval, games (Atari (http://karpathy.github.io/2016/05/31/rl/) and AlphaGo (https://deepmind.com/research/alphago/)), and beyond.