Long Short Term Memory
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Long Short Term Memory Networks With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2017-07-20
Long Short Term Memory Networks With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-20 with Computers categories.
The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems. In this laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about LSTMs. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what LSTMs are, and how to develop a suite of LSTM models to get the most out of the method on your sequence prediction problems.
Long Short Term Memory Networks With Python
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Author : Jason Brownlee
language : en
Publisher:
Release Date : 2017
Long Short Term Memory Networks With Python written by Jason Brownlee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Neural networks (Computer science) categories.
The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems. In this laser-focused Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math, research papers and patchwork descriptions about LSTMs. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what LSTMs are, and how to develop a suite of LSTM models to get the most out of the method on your sequence prediction problems.
Long Short Term Memory
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-06-26
Long Short Term Memory written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-26 with Computers categories.
What Is Long Short Term Memory Long short-term memory, often known as LSTM, is a type of artificial neural network that is utilized in the domains of deep learning and artificial intelligence. LSTM neural networks have feedback connections, in contrast to more traditional feedforward neural networks. This type of recurrent neural network, commonly known as an RNN, is capable of processing not only individual data points but also complete data sequences. Because of this property, LSTM networks are particularly well-suited for the processing and forecasting of data. For instance, LSTM can be used to perform tasks such as connected unsegmented handwriting identification, speech recognition, machine translation, speech activity detection, robot control, video game development, and healthcare. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Long short-term memory Chapter 2: Artificial neural network Chapter 3: Jürgen Schmidhuber Chapter 4: Recurrent neural network Chapter 5: Vanishing gradient problem Chapter 6: Sepp Hochreiter Chapter 7: Gated recurrent unit Chapter 8: Deep learning Chapter 9: Types of artificial neural networks Chapter 10: History of artificial neural networks (II) Answering the public top questions about long short term memory. (III) Real world examples for the usage of long short term memory in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of long short term memory. What Is Artificial Intelligence Series The Artificial Intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Supervised Sequence Labelling With Recurrent Neural Networks
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Author : Alex Graves
language : en
Publisher: Springer
Release Date : 2012-02-06
Supervised Sequence Labelling With Recurrent Neural Networks written by Alex Graves and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-06 with Technology & Engineering categories.
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.
Long Short Term Memory In Recurrent Neural Networks
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Author : Félix Gers
language : en
Publisher:
Release Date : 2001
Long Short Term Memory In Recurrent Neural Networks written by Félix Gers and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Supervised Machine Learning For Text Analysis In R
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Author : Emil Hvitfeldt
language : en
Publisher: CRC Press
Release Date : 2021-10-22
Supervised Machine Learning For Text Analysis In R written by Emil Hvitfeldt and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-22 with Computers categories.
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
Deep Learning Classifiers With Memristive Networks
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Author : Alex Pappachen James
language : en
Publisher: Springer
Release Date : 2019-04-08
Deep Learning Classifiers With Memristive Networks written by Alex Pappachen James and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-08 with Technology & Engineering categories.
This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors.
Deep Learning For Subtyping And Prediction Of Diseases
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Author : Hayrettin Okut
language : en
Publisher:
Release Date : 2019
Deep Learning For Subtyping And Prediction Of Diseases written by Hayrettin Okut and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Computers categories.
The long short-term memory neural network (LSTM) is a type of recurrent neural network (RNN). During the training of RNN architecture, sequential information is used and travels through the neural network from input vector to the output neurons, while the error is calculated and propagated back through the network to update the network parameters. Information in these networks incorporates loops into the hidden layer. Loops allow information to flow multi-directionally so that the hidden state signifies past information held at a given time step. Consequently, the output is dependent on the previous predictions which are already known. However, RNNs have limited capacity to bridge more than a certain number of steps. Mainly this is due to the vanishing of gradients which causes the predictions to capture the short-term dependencies as information from earlier steps decays. As more layers in RNN containing activation functions are added, the gradient of the loss function approaches zero. The LSTM neural networks (LSTM-ANNs) enable learning long-term dependencies. LSTM introduces a memory unit and gate mechanism to enable capture of the long dependencies in a sequence. Therefore, LSTM networks can selectively remember or forget information and are capable of learn thousands timesteps by structures called cell states and three gates.
Applications Of Artificial Intelligence In Process Systems Engineering
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Author : Jingzheng Ren
language : en
Publisher: Elsevier
Release Date : 2021-06-05
Applications Of Artificial Intelligence In Process Systems Engineering written by Jingzheng Ren and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-05 with Technology & Engineering categories.
Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering
Image Analysis With Long Short Term Memory Recurrent Neural Networks
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Author : Wonmin Byeon
language : en
Publisher:
Release Date : 2016
Image Analysis With Long Short Term Memory Recurrent Neural Networks written by Wonmin Byeon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.