Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

This is a timely book presenting an overview of the current state-of-the-art within established projects, presenting many different aspects of workflow from users to tool builders. It provides an overview of active research, from a number of different perspectives. It includes theoretical aspects of workflow and deals with workflow for e-Science as opposed to e-Commerce. The topics covered will be of interest to a wide range of practitioners.

Workflows for e-Science

https://www.cse.iitk.ac.in/users/isaha/Publications/Journals/NC10.pdf/

Artificial neural networks in hardware: A survey of two decades of progress

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Pulse coupled neural network (PCNN) models are described. The linking field modulation term is shown to be a universal feature of any biologically grounded dendritic model. Applications and implementations of PCNNs are reviewed. Application based variations and simplifications are summarized. The PCNN image decomposition (factoring) model is described in detail.

PCNN models and applications

Review of hardware neural networks: A User's perspective

Introduction Chapters 16 and 17 described the client/server or distributed object paradigm at a somewhat abstract level using the UML notation language. Because of the abstract nature of that discussion there was almost no Java code in either of those chapters. In fact, the only place that any code snippets appeared at all was to illustrate UML concepts in a familiar concrete Java environment. Now we will begin to explain how to really implement the distributed object paradigm using Java Remote Method Invocation (RMI). By the end of this chapter, we will have some real code that demonstrates simple communication between two distributed Java objects, though we still won't have a running application using the architecture of Chapters 16 and 17. That complete application will be developed in Chapter 20. Before getting to the real code, we must first describe how distributed computing can be implemented. The concepts are not particularly new, nor are they unique to Java. However, just as in other areas, the Java platform offers advantages and an easy programming style that are absent in other languages. These advantages are discussed as we explain how distributed computing works. While the full capabilities of RMI are beyond the scope of this book, we will learn the basics of RMI, and with these skills we can implement the distributed architecture described in Chapters 16 and 17. How distributed computing works Consider a conventional (non-distributed) computer program, be it written in Java or some other language.

JavaTech: Java Remote Method Invocation (RMI)

We survey the currently available neural network hardware, including VLSI chips (digital, analog, and hybrid), PC accelerator cards, and multi-board neurocomputers. We concentrate on commercial hardware, but also include a few prototypes of special interest. As examples of applications, some systems developed for high energy physics experiments that use this hardware are presented. © (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Survey of neural network hardware

Biologically inspired image/signal processing, in particular neural networks like the Pulse-Coupled Neural Network (PCNN), are revisited. Their use with high granularity high-energy physics detectors, as well as optical sensing devices, for filtering, de-noising, segmentation, object isolation and edge detection is discussed.

Intelligent detectors modelled from the cat's eye

Identification of star constellations with an onboard star tracker provides the highest precision of all attitude determination techniques for spacecraft. A method for identification of star constellations inspired by neural network (NNW) techniques is presented. It compares feature vectors derived from histograms of distances to multiple stars around the unknown star. The NNW method appears most robust with respect to position noise and would require a smaller database than conventional methods, especially for small fields of view. The neural network method is quite slow when performed on a sequential (serial) processor, but would provide very high speed if implemented in special hardware. Such hardware solutions could also yield lower low weight and low power consumption, both important features for small satellites.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Clark S. Lindsey

Papers

Review of hardware neural networks: A User's perspective

JavaTech: Java Remote Method Invocation (RMI)

Survey of neural network hardware

Intelligent detectors modelled from the cat's eye

Method for star identification using neural networks