%pylab inline import os from ipypublish import nb_setup
Populating the interactive namespace from numpy and matplotlib
There is a lot of excitement surrounding the fields of Neural Networks (NN) and Deep Learning (DL), due to numerous well-publicized successes that these systems have achieved in the last few years. The objective of this monograph is to provide a concise survey of this fast developing field, with special emphasis on more recent developments. We will use the nomenclature Deep Learning Networks (DLN) for Neural Networks that use Deep Learning algorithms.
DLNs form a subfield within the broader area of Machine Learning (ML). ML systems are defined as those that are able to train (or program) themselves, either by using a set of labeled training data (called Supervised Learning), or even in the absence of training data (called Un-Supervised Learning). In addition there is a related field called Reinforcement Learning in which algorithms are trained not by training examples, but by using a sequence of control actions and rewards. Even though ML systems are trained on a finite set of training data, their usefulness arises from the fact that they are able to generalize from these and process data that they have not seen before.
Most of the recent breakthroughs in DLNs have been in Supervised Learning systems, which are now being widely used in numerous applications in industrial and consumer settings, some of these are enumerated below:
Image Recognition and Object Detection: DLNs enable the detection and classification of objects in images into one of more than thousand different categories. This has hundreds of applications ranging from diagnosis in medical imaging to security related image analysis.
Speech Transcription: DLN systems are used to transcribe speech into text with a high level of accuracy. Advances in this area have enabled products such as Amazon's Alexa and Apple's Siri.
Machine Translation: Large scale translation systems such as the one deployed by Google, have switched over to using DLN algorithms Sutskever, Vinyals, Le (2014).
FinTech Applications: DLN systems are being used to automate applications such as Trading and Portfolio Management. Indeed the vast majority of transactions on Wall Street today are carried out using DLN based auto trading programs.
Image Recognition belongs to the class of applications that involve classification of the input into one of several categories. The Speech Transcription and Machine Translation applications involve not just recognition of the input pattern, but also the generation of patterns as part of the output (so called Generative Models).