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.

Download or read book Supervised Sequence Labelling with Recurrent Neural Networks written by and published by . This book was released on 2012-02-07 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning Fundamentals Theory and Applications written by Kaizhu Huang and published by Springer. This book was released on 2019-02-15 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.

Download or read book Deep Learning written by Josh Patterson and published by "O'Reilly Media, Inc.". This book was released on 2017-07-28 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop

Download or read book Computer Vision written by Jinfeng Yang and published by Springer. This book was released on 2017-11-29 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three volume set, CCIS 771, 772, 773, constitutes the refereed proceedings of the CCF Chinese Conference on Computer Vision, CCCV 2017, held in Tianjin, China, in October 2017. The total of 174 revised full papers presented in three volumes were carefully reviewed and selected from 465 submissions. The papers are organized in the following topical sections: biological vision inspired visual method; biomedical image analysis; computer vision applications; deep neural network; face and posture analysis; image and video retrieval; image color and texture; image composition; image quality assessment and analysis; image restoration; image segmentation and classification; image-based modeling; object detection and classification; object identification; photography and video; robot vision; shape representation and matching; statistical methods and learning; video analysis and event recognition; visual salient detection

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.

Download or read book Frontiers in Handwriting Recognition written by Utkarsh Porwal and published by Springer Nature. This book was released on 2022-11-25 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 18th International Conference on Frontiers in Handwriting Recognition, ICFHR 2022, which took place in Hyderabad, India, during December 4-7, 2022. The 36 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 61 submissions. The contributions were organized in topical sections as follows: Historical Document Processing; Signature Verification and Writer Identification; Symbol and Graphics Recognition; Handwriting Recognition and Understanding; Handwriting Datasets and Synthetic Handwriting Generation; Document Analysis and Processing.

Download or read book Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data written by Maosong Sun and published by Springer. This book was released on 2015-11-07 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th China National Conference on Computational Linguistics, CCL 2014, and of the Third International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2015, held in Guangzhou, China, in November 2015. The 34 papers presented were carefully reviewed and selected from 283 submissions. The papers are organized in topical sections on lexical semantics and ontologies; semantics; sentiment analysis, opinion mining and text classification; machine translation; multilinguality in NLP; machine learning methods for NLP; knowledge graph and information extraction; discourse, coreference and pragmatics; information retrieval and question answering; social computing; NLP applications.

Download or read book Natural Language Processing and Chinese Computing written by Xuanjing Huang and published by Springer. This book was released on 2018-01-03 with total page 966 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th CCF International Conference on Natural Language Processing, NLPCC 2017, held in Dalian, China, in November 2017. The 47 full papers and 39 short papers presented were carefully reviewed and selected from 252 submissions. The papers are organized around the following topics: IR/search/bot; knowledge graph/IE/QA; machine learning; machine translation; NLP applications; NLP fundamentals; social networks; and text mining.

Download or read book Deep Learning Concepts and Architectures written by Witold Pedrycz and published by Springer Nature. This book was released on 2019-10-29 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.

Download or read book MultiMedia Modeling written by Qi Tian and published by Springer. This book was released on 2015-12-31 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 9516 and 9517 constitutes the thoroughly refereed proceedings of the 22nd International Conference on Multimedia Modeling, MMM 2016, held in Miami, FL, USA, in January 2016. The 32 revised full papers and 52 poster papers were carefully reviewed and selected from 117 submissions. In addition 20 papers were accepted for five special sessions out of 38 submissions as well as 7 demonstrations (from 11 submissions) and 9 video showcase papers. The papers are organized in topical sections on video content analysis, social media analysis, object recognition and system, multimedia retrieval and ranking, multimedia representation, machine learning in multimedia, and interaction and mobile. The special sessions are: good practices in multimedia modeling; semantics discovery from multimedia big data; perception, aesthetics, and emotion in multimedia quality modeling; multimodal learning and computing for human activity understanding; and perspectives on multimedia analytics./div

Download or read book Artificial Neural Networks and Machine Learning ICANN 2019 Deep Learning written by Igor V. Tetko and published by Springer Nature. This book was released on 2019-09-09 with total page 807 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.

Download or read book Document Analysis and Recognition ICDAR 2021 Workshops written by Elisa H. Barney Smith and published by Springer Nature. This book was released on 2021-09-03 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the international workshops co-located with the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland, in September 2021.The total of 59 full and 12 short papers presented in this book were carefully selected from 96 contributions and divided into two volumes. Part I contains 29 full and 4 short papers that stem from the following meetings: ICDAR 2021 Workshop on Graphics Recognition (GREC); ICDAR 2021 Workshop on Camera-Based Document Analysis and Recognition (CBDAR); ICDAR 2021 Workshop on Arabic and Derived Script Analysis and Recognition (ASAR 2021); ICDAR 2021 Workshop on Computational Document Forensics (IWCDF). The main topics of the contributions are document processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; signature verification and document forensics, and others. “Accurate Graphic Symbol Detection in Ancient Document Digital Reproductions” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

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 497 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.

Download or read book Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics written by Sunil Kumar Dhal and published by John Wiley & Sons. This book was released on 2022-05-20 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.

Download or read book Statistical Modeling in Machine Learning written by Tilottama Goswami and published by Academic Press. This book was released on 2022-10-29 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more. Provides a comprehensive overview of the state-of-the-art in statistical concepts applied to Machine Learning with the help of real-life problems, applications and tutorials Presents a step-by-step approach from fundamentals to advanced techniques Includes Case Studies with both successful and unsuccessful applications of Machine Learning to understand challenges in its implementation, along with worked examples

Download or read book ARTIFICIAL NEURAL NETWORKS written by Dr. N.N. Praboo and published by Archers & Elevators Publishing House. This book was released on with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: