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

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely upto-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

Neural Networks And Learning Machines

1980 Preface * 1999 Preface * 1999 Acknowledgements * Introduction * 1 Circular Logic * 2 Phase Singularities (Screwy Results of Circular Logic) * 3 The Rules of the Ring * 4 Ring Populations * 5 Getting Off the Ring * 6 Attracting Cycles and Isochrons * 7 Measuring the Trajectories of a Circadian Clock * 8 Populations of Attractor Cycle Oscillators * 9 Excitable Kinetics and Excitable Media * 10 The Varieties of Phaseless Experience: In Which the Geometrical Orderliness of Rhythmic Organization Breaks Down in Diverse Ways * 11 The Firefly Machine 12 Energy Metabolism in Cells * 13 The Malonic Acid Reagent ('Sodium Geometrate') * 14 Electrical Rhythmicity and Excitability in Cell Membranes * 15 The Aggregation of Slime Mold Amoebae * 16 Numerical Organizing Centers * 17 Electrical Singular Filaments in the Heart Wall * 18 Pattern Formation in the Fungi * 19 Circadian Rhythms in General * 20 The Circadian Clocks of Insect Eclosion * 21 The Flower of Kalanchoe * 22 The Cell Mitotic Cycle * 23 The Female Cycle * References * Index of Names * Index of Subjects

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/596B270197FE27DE5FABB598B6210622/S0025557200150892a.pdf/div-class-title-span-class-italic-the-geometry-of-biological-time-span-by-a-t-winfree-pp-544-dm68-corrected-second-printing-1990-isbn-3-540-52528-9-springer-div.pdf

The geometry of biological time , by A. T. Winfree. Pp 544. DM68. Corrected Second Printing 1990. ISBN 3-540-52528-9 (Springer)

This study investigates deep learning based signal-to-noise ratio (SNR) estimation at the frame level. We propose to employ recurrent neural networks (RNNs) with long short-term memory (LSTM) in order to leverage contextual information for this task. As acoustic features are important for deep learning algorithms, we also examine a variety of monaural features and investigate feature combinations using Group Lasso and sequential floating forward selection. By replacing LSTM with bidirectional LSTM, the proposed algorithm naturally leads to a long-term SNR estimator. Systematical evaluations demonstrate that the proposed SNR estimators significantly outperform other frame-level and long-term SNR estimators.

Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning.

Supervised learning has exhibited great potential for speech separation in recent years. In this paper, we focus on separating target speech in reverberant conditions from binaural inputs using supervised learning. Specifically, deep neural network (DNN) is constructed to map from both spectral and spatial features to a training target. For spectral features extraction, we first convert binaural inputs into a single signal by applying a fixed beamformer. A new spatial feature is proposed and extracted to complement spectral features. The training target is the recently suggested ideal ratio mask (IRM). Systematic evaluations and comparisons show that the proposed system achieves good separation performance and substantially outperforms existing algorithms under challenging multisource and reverberant environments.

Binaural Reverberant Speech Separation Based on Deep Neural Networks.

Three neural models which process temporal information

The problem of overlapping harmonics is particularly acute in musical sound separation and has not been addressed adequately. We propose a monaural system based on binary time-frequency masking with an emphasis on robust decisions in time-frequency regions, where harmonics from different sources overlap. Our computational auditory scene analysis system exploits the observation that sounds from the same source tend to have similar spectral envelopes. Quantitative results show that utilizing spectral similarity helps binary decision making in overlapped time-frequency regions and significantly improves separation performance.

/pdf/musical-sound-separation-based-on-binary-time-frequency-2k7ypfgate.pdf

Musical Sound Separation Based on Binary Time-Frequency Masking

: Listeners' ability to understand a target speaker in the presence of one or more simultaneous competing speakers is subject to two types of masking: Energetic and informational. Energetic masking occurs when target and interfering signals overlap in time and frequency resulting in portions of target becoming inaudible. Informational masking occurs when the listener is unable to segregate the target from interference, while both are audible. We present a model of multitalker speech perception that accounts for both types of masking. Human perception in the presence of energetic masking is modeled using a speech recognizer that treats the masked time-frequency units of target as missing data. The effects of informational masking on the recognizer are modeled using the output of a speech segregation system. On a systematic evaluation, the performance of the proposed model is in broad agreement with perceptual results.

/pdf/modeling-the-perception-of-multitalker-speech-2ldqgxtts6.pdf

DeLiang Wang

Papers

Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning.

Binaural Reverberant Speech Separation Based on Deep Neural Networks.

Three neural models which process temporal information

Musical Sound Separation Based on Binary Time-Frequency Masking

Modeling the Perception of Multitalker Speech