Products related to Processing:
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Deep Learning for Natural Language Processing
Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context.Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively.In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key features An overview of NLP and deep learning * Models for textual similarity * Deep memory-based NLP * Semantic role labeling * Sequential NLP Audience For those with intermediate Python skills and general knowledge of NLP.No hands-on experience with Keras or deep learning toolkits is required. About the technology Natural language processing is the science of teaching computers to interpret and process human language.Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learning Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics.He's the technical coordinator of two large European Union-funded research security-related projects.He's currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.
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Machine Learning and Deep Learning in Natural Language Processing
Natural Language Processing (NLP) is a sub-field of Artificial Intelligence, linguistics, and computer science and is concerned with the generation, recognition, and understanding of human languages, both written and spoken.NLP systems examine the grammatical structure of sentences as well as the specific meanings of words, and then they utilize algorithms to extract meaning and produce results.Machine Learning and Deep Learning in Natural Language Processing aims at providing a review of current Neural Network techniques in the NLP field, in particular about Conversational Agents (chatbots), Text-to-Speech, management of non-literal content – like emotions, but also satirical expressions – and applications in the healthcare field. NLP has the potential to be a disruptive technology in various healthcare fields, but so far little attention has been devoted to that goal.This book aims at providing some examples of NLP techniques that can, for example, restore speech, detect Parkinson’s disease, or help psychotherapists. This book is intended for a wide audience. Beginners will find useful chapters providing a general introduction to NLP techniques, while experienced professionals will appreciate the chapters about advanced management of emotion, empathy, and non-literal content.
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Signal Processing and Machine Learning with Applications
Signal processing captures, interprets, describes and manipulates physical phenomena.Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information.This book presents different kinds of signals humans use and applies them for human machine interaction to communicate.Signal Processing and Machine Learning with Applications presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with their applications.It is organized in three parts: Realms of Signal Processing; Machine Learning and Recognition; and Advanced Applications and Artificial Intelligence.The comprehensive coverage is accompanied by numerous examples, questions with solutions, with historical notes.The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engagedwith signal processing, machine learning and the applications.
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Natural Language Processing : A Machine Learning Perspective
With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework.NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems.Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods.Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own.Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.
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Is working memory processing speed good for learning in school/studies?
Yes, working memory processing speed is important for learning in school/studies. It allows individuals to quickly take in information, process it, and retain it for further use. A faster processing speed can help students keep up with the pace of lessons, understand complex concepts more easily, and perform better on tasks that require quick thinking and problem-solving. Overall, a strong working memory processing speed can enhance academic performance and make learning more efficient.
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What is lifting processing?
Lifting processing refers to the act of extracting and transforming data from one source to another in order to make it usable for analysis or other purposes. This process involves cleaning, structuring, and enriching the data to ensure its accuracy and relevance. Lifting processing is essential for businesses and organizations to make informed decisions based on reliable and actionable data.
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What is plastic processing?
Plastic processing refers to the series of steps involved in converting raw plastic materials into finished products. This can include processes such as extrusion, injection molding, blow molding, and thermoforming. During plastic processing, the raw plastic material is heated, shaped, and cooled to create the desired product. This process is used to manufacture a wide range of plastic products, from packaging materials to automotive parts.
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What is order processing?
Order processing is the series of steps involved in receiving, verifying, and fulfilling customer orders. It includes tasks such as order entry, payment processing, inventory management, and shipping. Efficient order processing is crucial for ensuring customer satisfaction and timely delivery of products or services. It helps businesses streamline their operations and maintain accurate records of transactions.
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Machine Learning Algorithms : Adversarial Robustness in Signal Processing
This book demonstrates the optimal adversarial attacks against several important signal processing algorithms.Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks.Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing.Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks. The authors in this book mainly examine the adversarial robustness of three commonly used machine learning algorithms in signal processing respectively: linear regression, LASSO-based feature selection, and principal component analysis (PCA).As to linear regression, the authors derive the optimal poisoning data sample and the optimal feature modifications, and also demonstrate the effectiveness of the attack against a wireless distributed learning system.The authors further extend the linear regression to LASSO-based feature selection and study the best strategy to mislead the learning system to select the wrong features.The authors find the optimal attack strategy by solving a bi-level optimization problem and also illustrate how this attack influences array signal processing and weather data analysis.In the end, the authors consider the adversarial robustness of the subspace learning problem.The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm. This book targets researchers working in machine learning, electronic information, and information theory as well as advanced-level students studying these subjects.R&D engineers who are working in machine learning, adversarial machine learning, robust machine learning, and technical consultants working on the security and robustness of machine learning are likely to purchase this book as a reference guide.
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Graph Learning and Network Science for Natural Language Processing
Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method.This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL.It also contains information about language generation based on graphical theories and language models. Features:Presents a comprehensive study of the interdisciplinary graphical approach to NLPCovers recent computational intelligence techniques for graph-based neural network modelsDiscusses advances in random walk-based techniques, semantic webs, and lexical networksExplores recent research into NLP for graph-based streaming dataReviews advances in knowledge graph embedding and ontologies for NLP approachesThis book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.
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Deep Learning for Natural Language Processing : A Gentle Introduction
Deep Learning is becoming increasingly important in a technology-dominated world.However, the building of computational models that accurately represent linguistic structures is complex, as it involves an in-depth knowledge of neural networks, and the understanding of advanced mathematical concepts such as calculus and statistics.This book makes these complexities accessible to those from a humanities and social sciences background, by providing a clear introduction to deep learning for natural language processing.It covers both theoretical and practical aspects, and assumes minimal knowledge of machine learning, explaining the theory behind natural language in an easy-to-read way.It includes pseudo code for the simpler algorithms discussed, and actual Python code for the more complicated architectures, using modern deep learning libraries such as PyTorch and Hugging Face.Providing the necessary theoretical foundation and practical tools, this book will enable readers to immediately begin building real-world, practical natural language processing systems.
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Learning from Data Streams : Processing Techniques in Sensor Networks
Sensor networks consist of distributed autonomous devices that cooperatively monitor an environment.Sensors are equipped with capacities to store information in memory, process this information and communicate with their neighbors.Processing data streams generated from wireless sensor networks has raised new research challenges over the last few years due to the huge numbers of data streams to be managed continuously and at a very high rate. The book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system.The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education. This research monograph delivers to researchers and graduate students the state of the art in data stream processing in sensor networks.The huge bibliography offers an excellent starting point for further reading and future research.
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What does processing mean?
Processing refers to the act of transforming, manipulating, or analyzing data in order to derive meaningful insights or achieve a specific outcome. This can involve various activities such as organizing, sorting, filtering, and performing calculations on the data. In the context of computer systems, processing also refers to the execution of instructions by a CPU to carry out tasks such as data retrieval, computation, and communication. Overall, processing involves the systematic handling of information to generate desired results.
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What is the processing number?
The processing number is a unique identifier assigned to each transaction processed by a financial institution. It helps track and identify the transaction throughout the processing cycle. This number is crucial for resolving any issues related to the transaction and ensuring that it is completed accurately and efficiently. The processing number is typically included in the transaction details provided to the customer for reference.
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Does writing help in processing?
Yes, writing can be a helpful tool in processing thoughts and emotions. When we write things down, it can help us clarify our thoughts, organize our feelings, and gain a better understanding of ourselves. Writing can also provide a sense of release and catharsis, allowing us to express our emotions in a safe and constructive way. Overall, writing can be a valuable tool for processing and working through difficult experiences.
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What is Data Processing 2?
Data Processing 2 refers to the second stage in the data processing cycle, following Data Processing 1. It involves the conversion of raw data into meaningful information through various operations such as sorting, summarizing, calculating, and organizing. Data Processing 2 plays a crucial role in transforming data into a format that can be easily analyzed and used for decision-making purposes.
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