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Tree Based Convolutional Neural Networks
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Book Synopsis Tree-Based Convolutional Neural Networks by : Lili Mou
Download or read book Tree-Based Convolutional Neural Networks written by Lili Mou and published by Springer. This book was released on 2018-10-09 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data. TBCNNsare related to existing convolutional neural networks (CNNs) and recursive neural networks (RNNs), but they combine the merits of both: thanks to their short propagation path, they are as efficient in learning as CNNs; yet they are also as structure-sensitive as RNNs. In this book, readers will also find a comprehensive literature review of related work, detailed descriptions of TBCNNs and their variants, and experiments applied to program analysis and natural language processing tasks. It is also an enjoyable read for all those with a general interest in deep learning.
Book Synopsis Tree-Based Convolutional Neural Networks by : Lili Mou
Download or read book Tree-Based Convolutional Neural Networks written by Lili Mou and published by Springer. This book was released on 2018-10-01 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data. TBCNNsare related to existing convolutional neural networks (CNNs) and recursive neural networks (RNNs), but they combine the merits of both: thanks to their short propagation path, they are as efficient in learning as CNNs; yet they are also as structure-sensitive as RNNs. In this book, readers will also find a comprehensive literature review of related work, detailed descriptions of TBCNNs and their variants, and experiments applied to program analysis and natural language processing tasks. It is also an enjoyable read for all those with a general interest in deep learning.
Book Synopsis Tree Defects Image Classification Using Convolutional Neural Networks on a Small Dataset by : Arjun Dixit
Download or read book Tree Defects Image Classification Using Convolutional Neural Networks on a Small Dataset written by Arjun Dixit and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Neural Network Methods in Natural Language Processing by : Yoav Goldberg
Download or read book Neural Network Methods in Natural Language Processing written by Yoav Goldberg and published by Morgan & Claypool Publishers. This book was released on 2017-04-17 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
Book Synopsis Advances in Knowledge Discovery and Data Mining by : Jinho Kim
Download or read book Advances in Knowledge Discovery and Data Mining written by Jinho Kim and published by Springer. This book was released on 2017-04-25 with total page 876 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.
Book Synopsis Practical Deep Learning by : Ronald T. Kneusel
Download or read book Practical Deep Learning written by Ronald T. Kneusel and published by No Starch Press. This book was released on 2021-02-23 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
Book Synopsis Neural Network Methods for Natural Language Processing by : Yoav Goldberg
Download or read book Neural Network Methods for Natural Language Processing written by Yoav Goldberg and published by Springer Nature. This book was released on 2022-06-01 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
Book Synopsis Tree-based Machine Learning Algorithms by : Clinton Sheppard
Download or read book Tree-based Machine Learning Algorithms written by Clinton Sheppard and published by Createspace Independent Publishing Platform. This book was released on 2017-09-09 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learn how to use decision trees and random forests for classification and regression, their respective limitations, and how the algorithms that build them work. Each chapter introduces a new data concern and then walks you through modifying the code, thus building the engine just-in-time. Along the way you will gain experience making decision trees and random forests work for you."--Back cover.
Book Synopsis Introduction to Graph Neural Networks by : Zhiyuan Liu
Download or read book Introduction to Graph Neural Networks written by Zhiyuan Liu and published by Morgan & Claypool Publishers. This book was released on 2020-03-20 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.
Book Synopsis Tree-based Neural Net (TBNN) for Learning "difficult" Concepts by : Irena Ivanova
Download or read book Tree-based Neural Net (TBNN) for Learning "difficult" Concepts written by Irena Ivanova and published by . This book was released on 1994 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "This paper presents a new learning system and reports its first successful application to a medical domain. Polygraphic data were recorded during 8-hours sleep in 2 infants. A set of 15 parameters were estimated and submitted to an expert for classification. In this way, two data files were obtained and used as a test domain for the system. The tree-based neural net builds on the idea of generating a decision tree and translating it into a neural network architecture that is trainable by the backpropagation algorithm. The network has unambiguously defined topology and initial weights, and its subsequent training is very fast. The contribution of the system's individual aspects to the overall performance is studied. The achieved classification accuracy of cca 85% compares very favourably with other learning systems."
Book Synopsis Neural Network Guided Evolution of L-system Plants by : Chen Xuhao(Eric)
Download or read book Neural Network Guided Evolution of L-system Plants written by Chen Xuhao(Eric) and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Lindenmayer system is a parallel rewriting system that generates graphic shapes using several rules. Genetic programming (GP) is an evolutionary algorithm that evolves expressions. A convolutional neural network(CNN) is a type of neural network which is useful for image recognition and classification. The goal of this thesis will be to generate different styles of L-system based 2D images of trees from scratch using genetic programming. The system will use a convolutional neural network to evaluate the trees and produce a fitness value for genetic programming. Different architectures of CNN are explored. We analyze the performance of the system and show the capabilities of the combination of CNN and GP. We show that a variety of interesting tree images can be automatically evolved. We also found that the success of the system highly depends on CNN training, as well as the form of the GP's L-system language representation.
Book Synopsis Advances in Machine Learning/Deep Learning-based Technologies by : George A. Tsihrintzis
Download or read book Advances in Machine Learning/Deep Learning-based Technologies written by George A. Tsihrintzis and published by Springer Nature. This book was released on 2021-08-05 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.
Download or read book ICIDSSD 2022 written by M. Afshar Alam and published by European Alliance for Innovation. This book was released on 2023-05-16 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Conference on ICT for Digital, Smart, and Sustainable Development provides an annual platform for researchers, academicians, and professionals from across the world. ICIDSSD 22, held at Jamia Hamdard, New Delhi, India, is the second international conference of this series of conferences to be held annually. The conference majorly focuses on the recent developments in the areas relating to Information and Communication Technologies and contributing to Sustainable Development. ICIDSSD ’22 has attracted research papers pertaining to an array of exciting research areas. The selected papers cover a wide range of topics including but not limited to Sustainable Development, Green Computing, Smart City, Artificial Intelligence, Big Data, Machine Learning, Cloud Computing LT, ANN, Security, and Data Science. Papers have primarily been judged on originality, presentation, relevance, and quality of work. Papers that clearly demonstrate results have been preferred. After the formal process of peer review, the editorial board has finally selected the most relevant papers to be included in this volume. We are sure that these research works will enrich our knowledge and motivate us towards exploring the latest avenues in research. We would like to thank our Hon'ble Vice Chancellor, Prof. (Dr) M.Afshar Alarn, for his constant and commendable support extended to us toward the path of excellence. Alongside him, we would like to thank the Registrar, Mr. Syed Saud Akhtar, and other officials of the University for supporting this conference. We thank our esteemed authors for having shown confidence in us and entrusting us with the publication of their research papers. The success of the conference would not have been possible without the submission of their quality research works. We thank the members of the International Scientific Advisory Committee, Technical Program Committee and members of all the other committees for their advice, guidance, and efforts. Also, we are grateful to our technical partners and sponsors, viz. HNF, EAI, ISTE, AICTE, TIC, CSI, JETE, and DST for sponsorship and assistance. We also thank the Department of Higher Education, MHRD for the timely issuance of ISBN for the proceedings of the conference. Finally, we are thankful to all who have contributed to the success of this conference.
Book Synopsis Knowledge Science, Engineering and Management by : Franz Lehner
Download or read book Knowledge Science, Engineering and Management written by Franz Lehner and published by Springer. This book was released on 2016-10-04 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th International Conference on Knowledge Science, Engineering and Management, KSEM 2016, held in Passau, Germany, in October 2016. The 49 revised full papers presented together with 2 keynotes were carefully selected and reviewed from 116 submissions. The papers are organized in topical sections on Clustering and Classification; Text Mining and Lexical Analysis; Content and Document Analysis; Enterprise Knowledge; Formal Semantics and Fuzzy Logic; Knowledge Engineering; Knowledge Enrichment and Visualization; Knowledge Management; Knowledge Retrieval; Knowledge Systems and Security; Neural Networks and Artificial Intelligence; Ontologies; and Recommendation Algorithms and Systems.
Download or read book Machine Learning written by Jugal Kalita and published by CRC Press. This book was released on 2022-12-21 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples. Features: Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students, and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding, enabling further exploration Presents worked out suitable programming examples, thus ensuring conceptual, theoretical and practical understanding of the machine learning methods. This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth, within limits of what can be taught in a short period of time. Thus, the book can provide foundations that will empower a student to read advanced books and research papers.
Book Synopsis MapReduce Based Convolutional Neural Networks by : Jackie Leung
Download or read book MapReduce Based Convolutional Neural Networks written by Jackie Leung and published by . This book was released on 2018 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Convolutional neural networks (CNNs) have gained global recognition in advancing the field of artificial intelligence and have had great successes in a wide array of applications including computer vision, speech and natural language processing. However, due to the rise of big data and increased complexity of tasks, the efficiency of training CNNs have been severely impacted. To achieve state-of-art results, CNNs require tens to hundreds of millions of parameters that need to be fine-tuned, resulting in extensive training time and high computational cost. To overcome these obstacles, this thesis takes advantage of distributed frameworks and cloud computing to develop a parallel CNN algorithm. Close examination of the implementation of MapReduce based CNNs as well as how the proposed algorithm accelerates learning are discussed and demonstrated through experiments. Results reveal high accuracy in classification and improvements in speedup, scaleup and sizeup compared to the standard algorithm." -- page ii
Book Synopsis Software Source Code by : Raghavendra Rao Althar
Download or read book Software Source Code written by Raghavendra Rao Althar and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-07-19 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will focus on utilizing statistical modelling of the software source code, in order to resolve issues associated with the software development processes. Writing and maintaining software source code is a costly business; software developers need to constantly rely on large existing code bases. Statistical modelling identifies the patterns in software artifacts and utilize them for predicting the possible issues.
