Chapter 4 is devoted to deep autoencoders as a prominent example of the unsupervised deep learning techniques. Chapter 5 gives a major example in the hybrid deep network category, which is the ...

Deep Autoencoders model for Anomaly Detection "Objective: This project focuses on the application of Autoencoders in Deep Learning, particularly for learning compressed representations of data.

Last week we described the next stage of deep learning hardware developments in some detail, focusing on a few specific architectures that capture what the rapidly-evolving field of machine learning ...

The past few years researchers are rising dominance of deep learning and artificial intelligence in huge range of various applications like image and video compression. The paper provides a deep ...

Surveillance applications of Deep Learning. Basic classification and false alarm reduction are the first applications of Deep Learning for video analytics, but they are by no means the only ones. In ...

Classification of Malware programs using autoencoders based deep learning architecture and its application to the microsoft malware Classification challenge (BIG 2015) dataset Abstract: Distinguishing ...

Deep learning opens a new level of capabilities within the artificial intelligence realm, but its use has been limited to data scientists. Nowadays, finally, it may be ripe for "democratization ...

It is clear the use of deep learning applications is expanding. The combination of very large data sets, robust algorithms, and high-performance compute platforms enables a large variety of ...

Deep-learning applications require embedded systems able to provide high computing capabilities, flexibility, availability of advanced peripherals, and high performance while operating in real time.