Recurrent Neural Networks

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Recurrent Neural Networks

Author : Larry Medsker
Publisher : CRC Press
ISBN 13 : 9781420049176
Total Pages : 414 pages
Book Rating : 4.0/5 (491 download)

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Book Synopsis Recurrent Neural Networks by : Larry Medsker

Download or read book Recurrent Neural Networks written by Larry Medsker and published by CRC Press. This book was released on 1999-12-20 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendous interest in these networks drives Recurrent Neural Networks: Design and Applications, a summary of the design, applications, current research, and challenges of this subfield of artificial neural networks. This overview incorporates every aspect of recurrent neural networks. It outlines the wide variety of complex learning techniques and associated research projects. Each chapter addresses architectures, from fully connected to partially connected, including recurrent multilayer feedforward. It presents problems involving trajectories, control systems, and robotics, as well as RNN use in chaotic systems. The authors also share their expert knowledge of ideas for alternate designs and advances in theoretical aspects. The dynamical behavior of recurrent neural networks is useful for solving problems in science, engineering, and business. This approach will yield huge advances in the coming years. Recurrent Neural Networks illuminates the opportunities and provides you with a broad view of the current events in this rich field.

Supervised Sequence Labelling with Recurrent Neural Networks

Author : Alex Graves
Publisher : Springer
ISBN 13 : 3642247970
Total Pages : 148 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Supervised Sequence Labelling with Recurrent Neural Networks by : Alex Graves

Download or read book Supervised Sequence Labelling with Recurrent Neural Networks written by Alex Graves and published by Springer. This book was released on 2012-02-06 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.

Recurrent Neural Networks

Author : Fathi M. Salem
Publisher : Springer Nature
ISBN 13 : 3030899292
Total Pages : 130 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Recurrent Neural Networks by : Fathi M. Salem

Download or read book Recurrent Neural Networks written by Fathi M. Salem and published by Springer Nature. This book was released on 2022-01-03 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author’s approach enables strategic co-training of output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled to create designs tailoring proficient procedures for recurrent neural networks in their targeted applications.

Grokking Machine Learning

Author : Luis Serrano
Publisher : Simon and Schuster
ISBN 13 : 1617295914
Total Pages : 510 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Grokking Machine Learning by : Luis Serrano

Download or read book Grokking Machine Learning written by Luis Serrano and published by Simon and Schuster. This book was released on 2021-12-14 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.

Recurrent Neural Networks with Python Quick Start Guide

Author : Simeon Kostadinov
Publisher : Packt Publishing Ltd
ISBN 13 : 1789133661
Total Pages : 115 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis Recurrent Neural Networks with Python Quick Start Guide by : Simeon Kostadinov

Download or read book Recurrent Neural Networks with Python Quick Start Guide written by Simeon Kostadinov and published by Packt Publishing Ltd. This book was released on 2018-11-30 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework. Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow libraryApply long short-term memory unitsExpand your skills in complex neural network and deep learning topicsBook Description Developers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling. Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field. What you will learnUse TensorFlow to build RNN modelsUse the correct RNN architecture for a particular machine learning taskCollect and clear the training data for your modelsUse the correct Python libraries for any task during the building phase of your modelOptimize your model for higher accuracyIdentify the differences between multiple models and how you can substitute themLearn the core deep learning fundamentals applicable to any machine learning modelWho this book is for This book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.

Recurrent Neural Networks for Prediction

Author : Danilo Mandic
Publisher :
ISBN 13 :
Total Pages : 297 pages
Book Rating : 4.:/5 (12 download)

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Book Synopsis Recurrent Neural Networks for Prediction by : Danilo Mandic

Download or read book Recurrent Neural Networks for Prediction written by Danilo Mandic and published by . This book was released on 2003 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.? Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal architectur.

Recurrent Neural Networks for Short-Term Load Forecasting

Author : Filippo Maria Bianchi
Publisher : Springer
ISBN 13 : 3319703382
Total Pages : 74 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Recurrent Neural Networks for Short-Term Load Forecasting by : Filippo Maria Bianchi

Download or read book Recurrent Neural Networks for Short-Term Load Forecasting written by Filippo Maria Bianchi and published by Springer. This book was released on 2017-11-09 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.

Recurrent Neural Networks for Prediction

Author : Danilo P. Mandic
Publisher :
ISBN 13 :
Total Pages : 318 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Recurrent Neural Networks for Prediction by : Danilo P. Mandic

Download or read book Recurrent Neural Networks for Prediction written by Danilo P. Mandic and published by . This book was released on 2001 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks consist of interconnected groups of neurons which function as processing units. Through the application of neural networks, the capabilities of conventional digital signal processing techniques can be significantly enhanced.

Learning with Recurrent Neural Networks

Author : Barbara Hammer
Publisher : Springer
ISBN 13 : 9781447139591
Total Pages : 150 pages
Book Rating : 4.1/5 (395 download)

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Book Synopsis Learning with Recurrent Neural Networks by : Barbara Hammer

Download or read book Learning with Recurrent Neural Networks written by Barbara Hammer and published by Springer. This book was released on 2014-03-12 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Folding networks, a generalisation of recurrent neural networks to tree structured inputs, are investigated as a mechanism to learn regularities on classical symbolic data, for example. The architecture, the training mechanism, and several applications in different areas are explained. Afterwards a theoretical foundation, proving that the approach is appropriate as a learning mechanism in principle, is presented: Their universal approximation ability is investigated- including several new results for standard recurrent neural networks such as explicit bounds on the required number of neurons and the super Turing capability of sigmoidal recurrent networks. The information theoretical learnability is examined - including several contribution to distribution dependent learnability, an answer to an open question posed by Vidyasagar, and a generalisation of the recent luckiness framework to function classes. Finally, the complexity of training is considered - including new results on the loading problem for standard feedforward networks with an arbitrary multilayered architecture, a correlated number of neurons and training set size, a varying number of hidden neurons but fixed input dimension, or the sigmoidal activation function, respectively.

Neural Networks and Deep Learning

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319944630
Total Pages : 512 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Neural Networks and Deep Learning by : Charu C. Aggarwal

Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Convergence Analysis of Recurrent Neural Networks

Author : Zhang Yi
Publisher : Springer Science & Business Media
ISBN 13 : 1475738196
Total Pages : 244 pages
Book Rating : 4.4/5 (757 download)

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Book Synopsis Convergence Analysis of Recurrent Neural Networks by : Zhang Yi

Download or read book Convergence Analysis of Recurrent Neural Networks written by Zhang Yi and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the outstanding and pioneering research work of Hopfield on recurrent neural networks (RNNs) in the early 80s of the last century, neural networks have rekindled strong interests in scientists and researchers. Recent years have recorded a remarkable advance in research and development work on RNNs, both in theoretical research as weIl as actual applications. The field of RNNs is now transforming into a complete and independent subject. From theory to application, from software to hardware, new and exciting results are emerging day after day, reflecting the keen interest RNNs have instilled in everyone, from researchers to practitioners. RNNs contain feedback connections among the neurons, a phenomenon which has led rather naturally to RNNs being regarded as dynamical systems. RNNs can be described by continuous time differential systems, discrete time systems, or functional differential systems, and more generally, in terms of non linear systems. Thus, RNNs have to their disposal, a huge set of mathematical tools relating to dynamical system theory which has tumed out to be very useful in enabling a rigorous analysis of RNNs.

Qualitative Analysis and Synthesis of Recurrent Neural Networks

Author : Anthony Michel
Publisher : CRC Press
ISBN 13 : 9780824707675
Total Pages : 508 pages
Book Rating : 4.7/5 (76 download)

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Book Synopsis Qualitative Analysis and Synthesis of Recurrent Neural Networks by : Anthony Michel

Download or read book Qualitative Analysis and Synthesis of Recurrent Neural Networks written by Anthony Michel and published by CRC Press. This book was released on 2001-12-04 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Analyzes the behavior, design, and implementation of artificial recurrent neural networks. Offers methods of synthesis for associative memories. Evaluates the qualitative properties and limitations of neural networks. Contains practical applications for optimal system performance."

Deep Learning for Computer Vision

Author : Rajalingappaa Shanmugamani
Publisher : Packt Publishing Ltd
ISBN 13 : 1788293355
Total Pages : 304 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis Deep Learning for Computer Vision by : Rajalingappaa Shanmugamani

Download or read book Deep Learning for Computer Vision written by Rajalingappaa Shanmugamani and published by Packt Publishing Ltd. This book was released on 2018-01-23 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is for This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.

Adaptive Control with Recurrent High-order Neural Networks

Author : George A. Rovithakis
Publisher : Springer Science & Business Media
ISBN 13 : 1447107853
Total Pages : 203 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Adaptive Control with Recurrent High-order Neural Networks by : George A. Rovithakis

Download or read book Adaptive Control with Recurrent High-order Neural Networks written by George A. Rovithakis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.

Handbook on Neural Information Processing

Author : Monica Bianchini
Publisher : Springer Science & Business Media
ISBN 13 : 3642366570
Total Pages : 547 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Handbook on Neural Information Processing by : Monica Bianchini

Download or read book Handbook on Neural Information Processing written by Monica Bianchini and published by Springer Science & Business Media. This book was released on 2013-04-12 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.

Automatic Speech and Speaker Recognition

Author : Chin-Hui Lee
Publisher : Springer Science & Business Media
ISBN 13 : 1461313678
Total Pages : 524 pages
Book Rating : 4.4/5 (613 download)

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Book Synopsis Automatic Speech and Speaker Recognition by : Chin-Hui Lee

Download or read book Automatic Speech and Speaker Recognition written by Chin-Hui Lee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modeling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use of dynamic, programming based heuristic search methods to find the best word sequence in the lexical network corresponding to the spoken utterance. Automatic Speech and Speaker Recognition: Advanced Topics groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Although no explicit partition is given, the book is divided into five parts: Chapters 1-2 are devoted to technology overviews; Chapters 3-12 discuss acoustic modeling of fundamental speech units and lexical modeling of words and pronunciations; Chapters 13-15 address the issues related to flexibility and robustness; Chapter 16-18 concern the theoretical and practical issues of search; Chapters 19-20 give two examples of algorithm and implementational aspects for recognition system realization. Audience: A reference book for speech researchers and graduate students interested in pursuing potential research on the topic. May also be used as a text for advanced courses on the subject.

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX 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.