Deep Learning: Theory and Practice
Adam Cichocki, Tomaso Poggio, S. Osowski, Victor Lempitsky · Bulletin of the Polish Academy of Sciences Technical Sciences · 2018
Recent breakthroughs in the fields of artificial intelligence (AI) and machine learning (ML) have been largely triggered by the emergence of the wide class of the deep neural network (DNN) technology, especially of convolutional neural networks (CNN).DNNs have become a vehicle for a large number of potential applications and commercial ventures in computer vision, early diagnosis of some diseases, drug discovery, biomedical informatics, prediction, natural language processing, recommender systems, robotics, gaming and artificial creativity, to mention but a few.The renaissance of deep neural networks has both created an active frontier of research in machine learning and provided many advantages in a variety of applications, to the extent that the performance of DNNs in multi-class classification and verification tasks can be comparable or even better than what is achievable by humans.Deep learning methods continue to dominate the field of machine learning, and are now important in many research areas, especially in artificial intelligence.Thanks to the increasing computational power of computers and the development of new architectures of neural networks, research in this area is greatly accelerated.Deep neural networks, such as convolutional neural networks (CNN) or recurrent long short-term memory (LSTM) networks, have found wide applications in different fields of computer vision and pattern recognition.Examples of such applications include recognition and classification of objects existing in images, image restoration, real-time multi-person pose estimation, computer games, translation, voice generation, music composition, transferring styles from famous paintings, etc. Deep learning was found highly useful in bioengineering, in which we handle the very difficult problems of medical image and signal analysis.This Special Section of the Bulletin of the Polish Academy of Sciences on Technical Sciences is devoted to theoretical aspects of deep machine learning as well as practical applications in some areas of signal and image processing, particularly in bioengineering.