deep learning for natural language processing stephan raaijmakers

Deep Learning for Natural Language Processing - Stephan Raaijmakers

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 ... Unknown localization key: "more"

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Python for Natural Language Processing - Pierre M. Nugues

Since the last edition of this book (2014), progress has been astonishing in all areas of Natural Language Processing, with recent achievements in Text Generation that spurred a media interest going beyond the traditional academic circles.

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AI for Small Business - Phil Pallen

An essential guide for small business owners and entrepreneurs looking to use artificial intelligence to automate tasks, improve customer service, make better decisions, grow their businesses faster, and stay ahead of the AI curve. ChatGPT, machine learning, automation, natural language processing. Every day, it seems like there is a new AI term to learn and a new promise of how it will improve your work.But with tons of conflicting information, small business owners are left wondering exactly how to leverage AI technology to grow and, more importantly, stay competitive with larger companies. Cutting through the buzzwords and media frenzy, AI for Small Business is the road map to take you from overwhelmed to empowered. Opening with simple explanations of AI basics and clarification of myths, you are empowered to assess your goals to create a comprehensive AI strategy for your business—including information on selecting tools, a timeline for implementation, and ideas for scaling systems.This essential guide then walks you through practical AI applications for each department, informing you how to use AI to automate tasks, make better decisions, and grow your business in all areas including: -Sales -Marketing -Social media and content creation -Customer service -Finance and accounting -Operations and ... Unknown localization key: "more"

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Large Language Models - Stephan Raaijmakers

An in-depth history of Large Language Models—and what their ubiquity, disruption, and creativity mean from a wider sociopolitical perspective.In November 2022, ChatGPT swept the globe with a mixed frenzy of excitement and anxiety. Was this a step closer to reaching singularity or just another marvel in machine learning? Author Stephan Raaijmakers provides a comprehensive introduction to Large Language Models (LLMs), describing what exactly they are capable of from a technical and creative standpoint. This concise volume covers everything from the architecture of LLM neural networks to the limitations of LLMs to how our governments can regulate this technology. In explaining how exactly LLMs learn from data sets, Raaijmakers defangs the more sensational arguments we may be familiar with. Instead, he offers a more grounded approach to how this groundbreaking—and increasingly ubiquitous—form of artificial intelligence will shape our society for years to come.

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Getting Started with Natural Language Processing

Getting Started with Natural Language Processing is a hands-on guide filled with everything you need to get started with NLP in a friendly, understandable tutorial. Full of Python code and hands-on projects, each chapter provides a concrete example with practical techniques that you can put into practice right away. By following the numerous Python-based examples and real-world case studies, you’ll apply NLP to search applications, extracting meaning from text, sentiment analysis, user profiling, and more. When you’re done, you’ll have a solid grounding in NLP that will serve as a foundation for further learning. Key Features · Extracting information from raw text · Named entity recognition · Automating summarization of key facts · Topic labeling For beginners to NLP with basic Python skills. About the technology Natural Language Processing is a set of data science techniques that enable machines to make sense of human text and speech. Advances in machine learning and deep learning have made NLP more efficient and reliable than ever, leading to a huge number of new tools and resources. From improving search applications to sentiment analysis, the possible applications of NLP are vast and growing. Ekaterina Kochmar is an Affiliated Lecturer and a Senior Research Associate ... Unknown localization key: "more"

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Handbook of Natural Language Processing

The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second EditionGreater prominence of statistical approachesNew applications sectionBroader multilingual scope to include Asian and European languages, along with EnglishAn actively maintained wiki (http://handbookofnlp.cse.unsw.edu.au) that provides online resources, supplementary information, and up-to-date developmentsDivided into three sections, the book first surveys classical techniques, including both symbolic and empirical approaches. The second section focuses on statistical approaches in natural language processing. In the final section of the book, each chapter describes a particular class of application, from Chinese machine translation to information visualization to ontology construction to biomedical text mining. Fully updated with the latest developments in the field, this comprehensive, modern handbook emphasizes how to implement practical language processing tools in computational systems.

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Natural Language Processing in Action - Hannes Hapke, Lane Hobson, Howard Cole

Description Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. In Natural Language Processing in Action, readers explore carefully chosen examples and expand their machine''s knowledge which they can then apply to a range of challenges. Key Features • Easy-to-follow • Clear examples • Hands-on-guide Audience A basic understanding of machine learning and some experience with a modern programming language such as Python, Java, C++, or JavaScript will be helpful. About the technology Natural Language Processing (NLP) is the discipline of teaching computers to read more like people, and readers can see examples of it in everything from chatbots to the speech-recognition software on their phone. Hobson Lane has more than 15 years of experience building autonomous systems that make important decisions on behalf of humans. Hannes Hapke is an Electrical Engineer turned Data Scientist with experience in deep learning. Cole Howard is a carpenter and writer turned Deep Learning expert.

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Natural Language Processing with Python - Steven Bird

Offers an introduction to Natural Language Processing, the field that underpins a variety of language technologies, ranging from predictive text and email filtering to automatic summarization and translation. This book helps you learn how to write Python programs to work with large collections of unstructured text.

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Deep Learning for Crack-Like Object Detection - Heng-Da Cheng, Kaige Zhang

Computer vision-based crack-like object detection has many useful applications, such as inspecting/monitoring pavement surface, underground pipeline, bridge cracks, railway tracks etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried in complex, textured background with high diversity which make the crack detection very challenging. During the past a few years, deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems.This book discusses crack-like object detection problem comprehensively. It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods. It provides a detailed review of object detection problems and focuses on the most challenging problem, crack-like object detection, to dig deep into the deep learning method. It includes examples of real-world problems, which are easy to understand and could be a good tutorial for introducing computer vision and machine learning.

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Deep Learning for Cognitive Computing Systems

Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.

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Practical Deep Learning for Cloud and Mobile - Anirudh Koul, Siddha Ganju, Meher Kasam

This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach.

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Machine Learning in Elixir - Sean Moriarity

Stable Diffusion, ChatGPT, Whisper - these are just a few examples of incredible applications powered by developments in machine learning. Despite the ubiquity of machine learning applications running in production, there are only a few viable language choices for data science and machine learning tasks. Elixir''s Nx project seeks to change that. With Nx, you can leverage the power of machine learning in your applications, using the battle-tested Erlang VM in a pragmatic language like Elixir. In this book, you''ll learn how to leverage Elixir and the Nx ecosystem to solve real-world problems in computer vision, natural language processing, and more.The Elixir Nx project aims to make machine learning possible without the need to leave Elixir for solutions in other languages. And even if concepts like linear models and logistic regression are new to you, you''ll be using them and much more to solve real-world problems in no time.Start with the basics of the Nx programming paradigm - how it differs from the Elixir programming style you''re used to and how it enables you to write machine learning algorithms. Use your understanding of this paradigm to implement foundational machine learning algorithms from scratch. Go deeper and discover the power of ... Unknown localization key: "more"

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Machine Learning Refined - Aggelos K. Katsaggelos, Reza Borhani, Jeremy Watt

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.

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Learning A New Language For Dummies - Maria J. Cabrera-Puche

Strategies, tools, and motivation for learning a new language Learning A New Language For Dummies explains how you can create a personal plan to achieve your language learning goals. Get research-based suggestions for speeding up your language acquisition and learn about the benefits of leveling up your linguistic ability. Even if you've never studied a language before, this easy-to-understand guide will prepare you to pick the learning methods that will work best for you. You#ll also get an intro to the basics of how humans learn languages, so you can stay motivated, set realistic goals, and achieve success. No matter what language you want to learn, this Dummies guide will help you start off on the right foot. Choose a language learning approach that fits you and your lifestyleGet step-by-step guidance for making a plan and setting achievable goalsLearn techniques and strategies for learning quicker and retaining moreImprove your odds of success with a foundation of knowledge about the learning process Anyone considering learning a new language or refreshing their knowledge of a language—and language teachers, too—will love Learning a New Language For Dummies.

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Gestalt Language Processing - Alison Battye

This book invites the reader to explore Natural Language Acquisition for Gestalt Language Processors. It clearly sets out the stages of Gestalt Language Processing and the steps in therapy to effectively help neurodivergent children and young people to move on with their language development, supporting them to become independent and creative language users. A wealth of real-life examples and in-depth case studies brings theory to life and allows practitioners to apply the principles to the children they know. Chapters include: • A detailed description of each stage of Natural Language Acquisition and a summary of the research background. • Clear and comprehensive guides to scoring language samples and tracking progress. • AAC (Augmentative and Alternative Communication) options and supports for developing literacy. • Consideration of regulation and movement supports. • Handouts for use in practice, with extra content available online. Gestalt Language Processing is an invaluable resource for any Speech and Language Therapist, parent or teacher who is looking to further their knowledge and transform the language support they offer to autistic and neurodivergent children.

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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems - Qiang Ren, Yinpeng Wang

This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of the latest advanced frameworks and the corresponding physics applications are introduced.As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics.

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The Korean Case for Youth Language Learning

The Korean Case for Youth Language Learning is a collection of chapters which address the complexities at the intersection of Korean language learning, the global popularity of Korean culture, and Generation Z learners’ characteristics.

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Practical Deep Learning, 2nd Edition - Ronald T. Kneusel

If you''ve been curious about artificial intelligence and 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, 2nd Edition 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 Neighbours, 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. This second edition is thoroughly revised and updated, and adds ... Unknown localization key: "more"

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Grokking Deep Reinforcement Learning - Miguel Morales

Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You''ll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. • Foundational reinforcement learning concepts and methods • The most popular deep reinforcement learning agents solving high-dimensional environments • Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for ... Unknown localization key: "more"

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Deep Learning with PyTorch - Eli Stevens, Luca Antiga

Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you, and your deep learning skills, become more sophisticated. Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you''ll discover just how effective and fun PyTorch can be. Key features • Using the PyTorch tensor API • Understanding automatic differentiation in PyTorch • Training deep neural networks • Monitoring training and visualizing results • Interoperability with NumPy Audience Written for developers with some knowledge of Python as well as basic linear algebra skills. Some understanding of deep learning will be helpful, however no experience with PyTorch or other deep learning frameworks is required. About the technology PyTorch is a machine learning framework with a strong focus on deep neural networks. Because ... Unknown localization key: "more"

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Learning Deep Learning - Magnus Ekman

NVIDIA''s Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today''s exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience.After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus ... Unknown localization key: "more"

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System Design for Epidemics Using Machine Learning and Deep Learning

This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.

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Deep Learning - Joanne Quinn, Michael Fullan, Joanne J. McEachen

Engage the World Change the World Deep Learning has claimed the attention of educators and policymakers around the world. This book not only defines what deep learning is, but takes up the question of how to mobilize complex, whole-system change and transform learning for all students. Deep Learning is a global partnership that works to: transform the role of teachers to that of activators who design experiences that build global competencies using real-life problem solving; and supports schools, districts, and systems to shift practice and how to measure learning in authentic ways. This comprehensive strategy incorporates practical tools and processes to engage students, educators, and families in new partnerships and drive deep learning. Inside you’ll find: The Deep Learning Framework Vignettes and case studies from K-12 classrooms in 1,200 schools in seven countries Guidance for reaching disadvantaged and differently abled students Sample protocols and rubrics for assessment Videos demonstrating deep learning design and innovative leadership in practice Through learning partnerships, learning environments, new pedagogical practices, and leveraged digital skills, deep learning reaches students as never before — preparing them to be active, engaged participants in their future.

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Deep Reinforcement Learning in Action - Alexander Zai, Brandon Brown

Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Key features • Structuring problems as Markov Decision Processes • Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them • Applying reinforcement learning algorithms to real-world problems Audience You’ll need intermediate Python skills and a basic understanding of deep learning. About the technology Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously contributed to the success of AlphaGo but that’s not all it can do! Alexander Zai is a Machine Learning Engineer at Amazon AI working on ... Unknown localization key: "more"

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The Science of Deep Learning - Iddo Drori

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.

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Math and Architectures of Deep Learning - Krishnendu Chaudhury

The mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you''ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. about the technology It''s important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You''ll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you''ll be glad you can quickly identify and fix problems. about the book Math and Architectures of Deep Learning sets out the foundations of DL in a way that''s both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept ... Unknown localization key: "more"

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Deep Reinforcement Learning - Aske Plaat

Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world''s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects'' desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses ... Unknown localization key: "more"

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Understanding Deep Learning - Simon J.D. Prince

An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion modelsShort, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of modelsStreamlined presentation separates critical ideas from background context and extraneous detailMinimal mathematical prerequisites, extensive illustrations, and practice problems make challenging ... Unknown localization key: "more"

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101 Games and Activities for Children With Autism, Asperger’s and Sensory Processing Disorders - Tara Delaney

LEARNING THROUGH PLAYOne of the best ways for children with autism, Asperger''s, and sensory processing disorders to learn is through play. Children improve their motor skills, language skills, and social skills by moving their bodies and interacting with their environment. Yet the biggest challenges parents, teachers, and loved ones face with children on the autism spectrum or with sensory processing disorders is how to successfully engage them in play. Pediatric occupational therapist Tara Delaney provides the answer. In 101 Games and Activities for Children with Autism, Asperger''s, and Sensory Processing Disorders, she shows you how to teach your children by moving their bodies through play. These interactive games are quick to learn but will provide hours of fun and learning for your child. And many of the games can be played indoors or outdoors, so your child can enjoy them at home, outside, or on field trips.More than one hundred games that help your child:make eye-contact, stay focused, and strengthen his or her motor skillsassociate words with objects and improve language and numerical skillslearn how to interact with others, how to take turns, and other social skills needed for attending preschool and school

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Grokking Deep Learning - Andrew W Trask

Artificial Intelligence is the most exciting technology of the century, and Deep Learning is, quite literally, the “brain” behind the world’s smartest Artificial Intelligence systems out there. Grokking Deep Learning is the perfect place to begin the deep learning journey. Rather than just learning the “black box” API of some library or framework, readers will actually understand how to build these algorithms completely from scratch. Key Features:Build neural networks that can see and understand images Build an A.I. that will learn to defeat you in a classic Atari gameHands-on Learning Written for readers with high school-level math and intermediateprogramming skills. Experience with Calculus is helpful but notrequired. ABOUT THE TECHNOLOGY Deep Learning is a subset of Machine Learning, which is a field dedicated to the study and development of machines that can learn, often with the goal of eventually attaining general artificial intelligence.

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Deep Learning Crash Course - Benjamin Midtvedt, Jesus Pineda, Giovanni Volpe

Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

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Approaches to Learning and Teaching First Language English - Helen Rees-Bidder

A subject-specific guide for teachers to supplement professional development and provide resources for lesson planning. Approaches to learning and teaching First Language English is the result of collaboration between Cambridge University Press and Cambridge International Examinations. Considering the local and global contexts when planning and teaching an international syllabus, the title presents ideas for First Language English with practical examples that help put theory into context. Teachers can download online tools for lesson planning from our website. This book is ideal support for those studying professional development qualifications or international PGCEs.

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English for Everyone English Grammar Guide: French language edition

We may all speak the same language, but getting to grips with grammar is the ultimate challenge. You could be puzzled by prepositions, confused by comparatives, or muddled over modals. Thankfully, this complete visual aid to everything in the English language sets you straight with a clear and concise format for easy understanding.The rules of English grammar are beautifully presented with eye-catching illustrations, step-by-step graphics, and straightforward explanations to help you learn. Suitable for English language learners at all levels, including experienced English speakers looking for a recap of key language points, English Grammar Guide covers basic, intermediate, and advanced grammar. There is no stone left unturned when it comes to the English language.All kinds of problems are solved, including tenses, verbs, adverbs, clauses, superlatives, and questions. You are encouraged to spot patterns and sequences in language to see the similarities and develop greater understanding. After swotting up, test yourself with a range of speaking, reading, and writing exercises to see how far you have come.This essential grammar guide is part of DK's English for Everyone series, an exciting and educational self-study course to build up confidence and fluency. Whether you want to improve your grammar for school, study, exams ... Unknown localization key: "more"

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Visible Learning for Social Studies, Grades K-12 - Julie Stern, John Hattie, Douglas Fisher, Nancy Frey

Help students move from surface-level learning to the transfer of understanding. How do social studies teachers maximize instruction to ensure students are prepared for an informed civic life? VISIBLE LEARNING® for Social Studies, Grades K-12 shows how the field is more than simply memorizing dates and facts—it encapsulates the skillful ability to conduct investigations, analyze sources, place events in historical context, and synthesize divergent points of view. The Visible Learning framework demonstrates that learning is not an event, but rather a process in which students move from surface-level learning to deep learning, and then onto the transfer of concepts, skills, and strategies. Encouraging learners to explore different facets of society, history, geography, and more, best practices for applying visible learning to social studies curriculum are presented through: ·        A scaffolded approach, including surface-level learning, deep learning, and transfer of learning ·        Examples of strategies, lessons, and activities best suited for each level of learning ·        Planning tools, rubrics, and templates to guide instruction Teachers must understand the impact they have on students and select approaches to maximize that impact. ... Unknown localization key: "more"

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Generative Deep Learning - David Foster

Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch

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Machine Learning For Dummies - John Paul Mueller, Luca Massaron

The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning—the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you'll dive into the tools, code, and math that make machine learning go—and you'll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to. Piece together what machine learning is, what it can do, and what it can't doLearn the basics of machine learning code and how it integrates with large datasetsUnderstand the mathematical principles that AI uses to make itself smarterConsider real-world applications of machine learning and write your own algorithms With clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.

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Visible Learning for Mathematics, Grades K-12 - Douglas Fisher, Nancy Frey, John Hattie, Sara Delano Moore, Linda M. Gojak, William Mellman

Selected as the Michigan Council of Teachers of Mathematics winter book club book! Rich tasks, collaborative work, number talks, problem-based learning, direct instruction…with so many possible approaches, how do we know which ones work the best? In Visible Learning for Mathematics, six acclaimed educators assert it’s not about which one—it’s about when—and show you how to design high-impact instruction so all students demonstrate more than a year’s worth of mathematics learning for a year spent in school. That’s a high bar, but with the amazing K-12 framework here, you choose the right approach at the right time, depending upon where learners are within three phases of learning: surface, deep, and transfer. This results in "visible" learning because the effect is tangible. The framework is forged out of current research in mathematics combined with John Hattie’s synthesis of more than 15 years of education research involving 300 million students. Chapter by chapter, and equipped with video clips, planning tools, rubrics, and templates, you get the inside track on which instructional strategies to use at each phase of the learning cycle: Surface learning phase: When—through carefully constructed experiences—students explore new concepts and make connections to procedural skills and vocabulary that give shape ... Unknown localization key: "more"

Objev podobné jako Visible Learning for Mathematics, Grades K-12 - Douglas Fisher, Nancy Frey, John Hattie, Sara Delano Moore, Linda M. Gojak, William Mellman

Machine Learning for Business Analytics - Peter Gedeck, Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Inbal Yahav

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using RAn expanded chapter focused on discussion of deep learning techniquesA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted ... Unknown localization key: "more"

Objev podobné jako Machine Learning for Business Analytics - Peter Gedeck, Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Inbal Yahav

Inspiring Deep Learning with Metacognition - Nathan Burns

Understand what metacognition is and how you can apply it to your secondary school teaching to support deep and effective learning in your classroom. Metacognition is a popular topic in teaching and learning debates, but it’s rarely clearly defined and can be difficult for teachers to understand how it can be applied in the classroom. This book offers a clear introduction to applying metacognition in secondary teaching, exploring the ‘what’, ‘when/how’ and ‘why’ of using metacognition in classrooms with real life examples of how this works in practice. This is a detailed and accessible resource that offers guidance that teachers can start applying to their own lesson planning immediately, across secondary subjects. Nathan Burns is the founder of @MetacognitionU and has written metacognitive teaching resources for TES and Oxford University Press. He is Head of Maths in a Derbyshire school. Â

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Machine Learning for Business Analytics

Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing at a CAGR of 44.06% between 2017 and 2024. The authors have compiled important knowledge on machine learning real-time applications in business analytics. This book enables readers to get broad knowledge in the field of machine learning models and to carry out their future research work. The future trends of ... Unknown localization key: "more"

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Practical Grammar Teaching for the Second Language Classroom

Practical Grammar Teaching for the Second Language Classroom provides a well-rounded foundation for teaching second language (L2) grammar for pre-service, novice, and practicing teachers, as well as for teacher educators who seek to develop their professional knowledge and skills. Written in a highly readable style for an international audience, it presents classroom strategies, techniques, activities, and applications of current and effective innovations to English grammar instruction.The contributors to this volume are well-established and highly regarded experts in L2 pedagogy, and each contributor offers a thorough overview of the principled and research-based instruction currently prevalent in teaching L2 grammar worldwide. The book provides practical guidelines and strategies for focused language instruction, teaching grammar in writing classes, and applications of technology to grammar teaching and learning.This key text is essential for students in undergraduate and graduate MA-TESOL (Master of Arts for Teaching English to Speakers of Other Languages)Â programs, pre-service and practicing ESL/EFL (English as a second language/English as a foreign language)Â teachers, teacher educators, and teaching faculty.

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Deep Learning - Andrew Glassner

Deep Learning: A Visual Approach helps demystify the algorithms that enable computers to drive cars, win chess tournaments, and create symphonies, while giving readers the tools necessary to build their own systems to help them find the information hiding within their own data, create ''deep dream'' artwork, or create new stories in the style of their favorite authors.

Objev podobné jako Deep Learning - Andrew Glassner

Visible Learning for Teachers - John Hattie

In November 2008, John Hattie’s ground-breaking book Visible Learning synthesised the results of more than fifteen years research involving millions of students and represented the biggest ever collection of evidence-based research into what actually works in schools to improve learning.Visible Learning for Teachers takes the next step and brings those ground breaking concepts to a completely new audience. Written for students, pre-service and in-service teachers, it explains how to apply the principles of Visible Learning to any classroom anywhere in the world. The author offers concise and user-friendly summaries of the most successful interventions and offers practical step-by-step guidance to the successful implementation of visible learning and visible teaching in the classroom.This book:links the biggest ever research project on teaching strategies to practical classroom implementationchampions both teacher and student perspectives and contains step by step guidance including lesson preparation, interpreting learning and feedback during the lesson and post lesson follow upoffers checklists, exercises, case studies and best practice scenarios to assist in raising achievementincludes whole school checklists and advice for school leaders on facilitating visible learning in their institutionnow includes additional meta-analyses bringing the total cited within the research to over 900comprehensively covers numerous areas of learning activity including pupil ... Unknown localization key: "more"

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Learning for Adaptive and Reactive Robot Control - Aude Billard, Sina Mirrazavi

Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises.This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots;pencil-and-paper and programming exercises;lecture videos, slides, and MATLAB code examples ... Unknown localization key: "more"

Objev podobné jako Learning for Adaptive and Reactive Robot Control - Aude Billard, Sina Mirrazavi

Machine Learning for Business Analytics - Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Kuber R. Deokar

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning—also known as data mining or predictive analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This fourth edition of Machine Learning for Business Analytics also includes: An expanded chapter on deep learningA new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniquesEnd-of-chapter ... Unknown localization key: "more"

Objev podobné jako Machine Learning for Business Analytics - Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Kuber R. Deokar

Quality Learning for Positive Ageing - Alan Potter

Quality Learning for Positive Ageing explores the views of older adult learners to understand the factors that contribute to ‘quality’ in later-life learning and how these relate to wellbeing, positive ageing, and gaining protection against cognitive decline.Through capturing and considering the viewpoints of learners, facilitators and learning organisations, the author outlines the specific characteristics of quality that they associate with informal learning and how it can be enhanced through the adoption of simple strategies. Key topics covered include the implications of an increasing ageing population and barriers to older people learning as well as the cognitive, mental wellbeing, health, and social benefits of learning in later life. Illustrated throughout with vignettes of real later-life learners, this thought-provoking text unpicks how learners can maximise the benefits of learning in later life for themselves, how tutors can create learning opportunities that embody the characteristics of quality for them, and how providers can offer an environment that simply allows quality learning to flourish.This accessible and comprehensive text will be of great interest to researchers of gerontology and ageing, educational gerontology, adult education, and lifelong learning as well as those engaged in dementia research.

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Digital and Postdigital Learning for Changing Universities - Savin-Baden Maggi

This book explores the purpose, role and function of the university and examines the disconnection between studentsÂ’ approaches to learning and university strategy. It centres on the idea that it is vital to explore what counts as a university in the twenty-first century, what it is for, and for whom, as well as how it can transcend social divisions. The universities of the twenty-first century need to have larger audiences, a broader voice, a shift away from othering and an effective means of progressing such shifts. What is central to such exploration is the idea that learning needs to be seen as postdigital. With a focus on how the growth of technology has and continues to affect university learning, this book:explores the concepts of the digital and the postdigitalpromotes just and inclusive pedagogies for higher educationconsiders ways to ensure learning is an ethical and political experiencestudies how to understand community and collective values through higher educationsuggests ways of promoting personal and collective responsibility for our world and its peoplespresents ways in which the university can challenge ideologies based on capitalist modes of consumption, privilege and exploitationDigital and Postdigital Learning for Changing Universities is essential reading for anyone seeking to reimagine ... Unknown localization key: "more"

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Deep learning v jazyku Python (978-80-247-3100-1)

Elektronická kniha - autor François Chollet, 328 stran, česky Strojové učení zaznamenalo v posledních letech pozoruhodný pokrok od téměř nepoužitelného rozpoznávání řeči a obrazu k nadlidské přesnosti. Od programů, které nedokázaly porazit jen trochu zkušenějšího hráče go, jsme dospěli k přemožiteli mistra světa. Za pokrokem ve vývoji učících se programů stojí tzv. hluboké učení (deep learning) – kombinace technických vylepšení, osvědčených postupů a teorií, které umožnily vyvinout množství dříve nerealizovatelných inteligentních aplikací. S jejich pomocí pak můžeme například analyzovat text či mluvené slovo, překládat z jazyka do jazyka, rozpoznávat osoby na sociálních sítích nebo používat samořídící automobily. Tato kniha naučí čtenáře navrhovat hluboce se učící systémy v jazyku Python, který je v současnosti nejpoužívanějším programovacím jazykem pro vývoj těchto systémů, a knihovny Keras a TensorFlow používané většinou vítězů soutěží systémů pro...

Objev podobné jako Deep learning v jazyku Python (978-80-247-3100-1)

Deep learning v jazyku Python - 2., rozšířené vydání - François Chollet

Strojové učení zaznamenalo v posledních letech pozoruhodný pokrok a dospělo od téměř nepoužitelného rozpoznávání řeči a obrazu k téměř nadlidské přesnosti, od programů, které nedokázaly porazit jen trochu zkušenějšího hráče šachu, až k přemožitelům mistrů světa.Za pokrokem ve vývoji učících se programů stojí tzv. hluboké učení (deep learning), což je kombinace teorií a osvědčených technických postupů, které umožnily vyvinout řadu dříve nerealizovatelných aplikací. S jejich pomocí můžeme analyzovat a syntetizovat text i mluvené slovo, překládat z jazyka do jazyka, rozpoznávat osoby nebo ovládat samořídící automobily.Kniha naučí čtenáře, jehož znalosti jazyka Python jsou na střední úrovni, navrhovat v tomto jazyku hluboce se učící systémy s pomocí knihoven Keras a TensorFlow, které používá většina autorů vítězných systémů ze soutěží v hlubokém učení. Výklad je založený na intuitivních vysvětleních a praktických příkladech. Náročné koncepty si procvičíte na aplikacích v oblasti počítačového vidění, zpracování přirozeného jazyka a generativních modelů. Získáte tak znalosti a praktické dovednosti, které vám umožní aplikovat hluboké učení ve vlastních projektech.Autorem knihy je François Chollet, tvůrce knihovny Keras a výzkumník v oblasti umělé inteligence společnosti Google. Výklad základních principů hlubokého učení i pokročilých dovedností Tvorba systému hlubokého učení pro počítačové vidění, časové řady, text i generování vlastních výtvorů (například obrázků) Způsob ... Unknown localization key: "more"

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180 Daysâ„¢: Social-Emotional Learning for Kindergarten - Jodene Smith, Brenda A. Van Dixhorn

This social and emotional learning (SEL) workbook for kindergarten students provides daily activities to learn about emotions, actions, relationships, and decision making.180 Days™: Social-Emotional Learning for KindergartenUses daily activities to promote students’ self-awareness, analyze relationships, discover diverse perspectives, and apply what they have learnedBuilds student''s confidence in self-reflection and growth through the use of fiction and nonfiction textsMakes at-home learning, whole class instruction, or small group support, quick and easyConnections will be made to the CASEL competencies, mindfulness, and key affective education initiativesParents appreciate the teacher-approved activity books that keep their child engaged and learning. Great for homeschooling, to reinforce learning at school, and build connections between home and school.Teachers rely on the daily practice workbooks to save them valuable time. The ready to implement activities are perfect to introduce SEL topics for discussion.

Objev podobné jako 180 Daysâ„¢: Social-Emotional Learning for Kindergarten - Jodene Smith, Brenda A. Van Dixhorn