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 â ¢ Models for textual similarity â ¢ Deep memory-based NLP â ¢ Semantic role labeling â ¢ 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. Heâ ™s the technical coordinator of two large European Union-funded research security-related projects. Heâ ™s currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.

Objev podobné jako Deep Learning for Natural Language Processing - Stephan Raaijmakers

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 â ¢ Easy-to-follow â ¢ Clear examples â ¢ 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.

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

Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems.

Objev podobné jako Deep Learning for Biology - Charles Ravarani, Natasha Latysheva

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.

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

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 languageâ ”and language teachers, tooâ ”will love Learning a New Language For Dummies.

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

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.

Objev podobné jako Gestalt Language Processing - Alison Battye

The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better. - Todd Cook The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher François Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You ll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable. about the technologyMachine learning has made remarkable progress in recent years. We ve gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn t beat a serious Go player, to defeating a world champion. Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learning⠔a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sector about the bookDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You ll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You ll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you ll have the knowledge and hands-on skills to apply deep learning in your own projects. what s insideDeep learning from first principlesImage-classification, imagine segmentation, and object detectionDeep learning for natural language processingTimeseries forecastingNeural style transfer, text generation, and image generation about the readerReaders need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. about the authorFrançois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does AI research, with a focus on abstraction and reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

Objev podobné jako Deep Learning with Python - François Chollet

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 six new chapters to further your exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine tuning, transfer learning, object detection, semantic segmentation, multilabel classification, self-supervised learning, generative adversarial networks, and large language models. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning, 2nd Edition will give you the skills and confidence to dive into your own machine learning projects.

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

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. â ¢ Foundational reinforcement learning concepts and methods â ¢ The most popular deep reinforcement learning agents solving high-dimensional environments â ¢ 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 maximum long-term return.

Objev podobné jako Grokking Deep Reinforcement Learning - Miguel Morales

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 â ¢ Using the PyTorch tensor API â ¢ Understanding automatic differentiation in PyTorch â ¢ Training deep neural networks â ¢ Monitoring training and visualizing results â ¢ 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 it emphasizes GPU-based acceleration, PyTorch performs exceptionally well on readily-available hardware and scales easily to larger systems. Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch.

Objev podobné jako Deep Learning with PyTorch - Eli Stevens, Luca Antiga

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 successâ ”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 Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images.Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA s invention of the GPU sparked the PC gaming market. The company s pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others.Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Objev podobné jako Learning Deep Learning - Magnus Ekman

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.

Objev podobné jako Deep Learning - Joanne Quinn, Michael Fullan, Joanne J. McEachen

Humans learn best from feedbackâ ”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 youâ ™ll need to implement it into your own projects. Key features â ¢ Structuring problems as Markov Decision Processes â ¢ Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them â ¢ Applying reinforcement learning algorithms to real-world problems Audience Youâ ™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 thatâ ™s not all it can do! Alexander Zai is a Machine Learning Engineer at Amazon AI working on MXNet that powers a suite of AWS machine learning products. Brandon Brown is a Machine Learning and Data Analysis blogger at outlace.com committed to providing clear teaching on difficult topics for newcomers.

Objev podobné jako Deep Reinforcement Learning in Action - Alexander Zai, Brandon Brown

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 or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You ll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you re done, you ll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge.

Objev podobné jako Math and Architectures of Deep Learning - Krishnendu Chaudhury

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.

Objev podobné jako Grokking Deep Learning - Andrew W Trask

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 (including TOEFL and IELTS), work, or travel, this is the perfect reading companion.

Objev podobné jako English for Everyone English Grammar Guide: French language edition

In November 2008, John Hattieâ ™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 motivation, curriculum, meta-cognitive strategies, behaviour, teaching strategies, and classroom managementVisible Learning for Teachers is a must read for any student or teacher who wants an evidence based answer to the question; â ˜how do we maximise achievement in our schools?â ™

Objev podobné jako Visible Learning for Teachers - John Hattie

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 factsâ ”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. This book will guide you through the process of identifying the right strategy for the right time to successfully move students through surface, deep, and transfer learning. Â

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

The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you ll find machine learningâ ”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 goâ ”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.

Objev podobné jako Machine Learning For Dummies - John Paul Mueller, Luca Massaron

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 to developing conceptual understandings. Deep learning phase: When⠔through the solving of rich high-cognitive tasks and rigorous discussion⠔students make connections among conceptual ideas, form mathematical generalizations, and apply and practice procedural skills with fluency. Transfer phase: When students can independently think through more complex mathematics, and can plan, investigate, and elaborate as they apply what they know to new mathematical situations. To equip students for higher-level mathematics learning, we have to be clear about where students are, where they need to go, and what it looks like when they get there. Visible Learning for Math brings about powerful, precision teaching for K-12 through intentionally designed guided, collaborative, and independent learning.

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 Machine learning â ”also known as data mining or data analyticsâ ” 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 to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presentedA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

Objev podobné jako 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 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 exercises that help readers gauge and expand their comprehension and competency of the material presentedA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

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

MACHINE LEARNING FOR BUSINESS ANALYTICS An up-to-date introduction to a market-leading platform for data analysis and machine learning Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing usersâ ™ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. readers will also find: Updated material which improves the bookâ ™s usefulness as a reference for professionals beyond the classroomFour new chapters, covering topics including Text Mining and Responsible Data ScienceAn updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbookA guide to JMP Pro s new features and enhanced functionality Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.

Objev podobné jako Machine Learning for Business Analytics - Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Muralidhara Anandamurthy, Mia L. Stephens

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

Objev podobné jako Generative Deep Learning - David Foster

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

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) ndash; 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)

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 fungování moderních AI systémů typu ChatGPT Popis rozdílů při spouštění programů na CPU, GPU a FPU Práce s webovým prostředím Collaboration, které umožňuje používat GPU a FPU na serveru

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

Whether they are new or experienced, teachers are expected to plan and deliver high-quality computing lessons to their pupils. Computing and Digital Learning for Primary Teachers provides an accessible introduction to teaching computing effectively and for deeper understanding in the primary classroom.Filled with practical resources to support lesson design, long-term planning, and assessment, readers will benefit from building their subject knowledge and learning to create engaging lessons for their pupils. Chapters explore:Supporting computational thinking and problem-solving to teach our pupils how to solve problems logically and systematically.Developing pupilsâ ™ digital literacy and use of IT, creating exciting opportunities for childrenâ ™s digital self-expression through film, animation, and 3D design.Managing technology in our schools, such as setting up and maintaining a virtual learning environment (VLE).Cross-curriculum links with STEAM and engineering, allowing children to solve real-world problems by combining their digital literacy with their knowledge of maths, science, and technology.Cost-effective and accessible ways of introducing physical computing and robotics to children.Safe and responsible uses of artificial intelligence (AI) in our primary schools.This essential resource provides a highly practical guide to delivering effective computing lessons in the primary classroom and is a must read for anyone who wishes to become a more confident and knowledgeable computing teacher.

Objev podobné jako Computing and Digital Learning for Primary Teachers - Owen Dobbing

A 28 cm átmérőjű Le Creuset kamenina tortaforma a Deep Teal színben készült. A forma kiválóan alkalmas torták, piték és quiche-k sütésére, egyenletes hőelosztást biztosít. Hosszú távú hőtartása, könnyű tisztíthatósága és esztétikus megjelenése mindennapi és ünnepi használatra egyaránt ideális.

• Kiváló minőségű kameninából készült, amely egyenletes hőelosztást és tökéletes sütési eredményt biztosít.
• Hullámos széle professzionális megjelenést kölcsönöz a süteményeknek, ideális ünnepi alkalmakra.
• Széles hőmérsékleti toleranciája (-23°C-tól +260°C-ig) lehetővé teszi a sütőben, mikróban, fagyasztóban való használatot és mosogatógépben való tisztítást.
• A mély türkiz (Deep Teal) szín és a luxus megjelenés bármely konyhát vagy asztaldíszítést felemel.

Objev podobné jako Forma na koláč 28 cm, DEEP TEAL, kamenina, Le Creuset

Exam Board: CCEALevel: GCSESubject: Learning for LifeFirst Teaching: September 2017First Exam: June 2019Enable students to critically engage with the new content and assessment requirements with this fully updated edition of the market-leading Student s Book for CCEA GCSE Learning for Life and Work- Provides complete coverage of the new content and assessment requirements with support at every stage from experienced teachers and subject experts David McVeigh, Michaella McAllister and Amanda McAleer- Prepares students for assessment with skills-building activities, practice questions and structured guidance on how to approach questions successfully- Helps engage students through accessible diagrams, research activities and a bank of up-to-date case study material- Develops subject knowledge through clear and detailed coverage of the key content structured around the specification

Objev podobné jako CCEA GCSE Learning for Life and Work Second Edition - Amanda McAleer, Michaella McAllister, Joanne McDonnell

The ACTFL Guide for Professional Language Educators: The Keys to Strategies for Language Instruction Engagement, Relevance, Critical Thinking, Collaboration

Objev podobné jako The Keys to Strategies for Language Instruction - David McAlpine

Exam board: CCEALevel: GCSESubject: CitizenshipFirst teaching: September 2017First exams: Summer 2019Target success in CCEA GCSE Learning for Life and Work with this proven formula for effective, structured revision; key content coverage is combined with exam-style tasks and practical tips to create a revision guide that students can rely on to review, strengthen and test their knowledge.With My Revision Notes, every student can:- Plan and manage a successful revision programme using the topic-by-topic planner- Consolidate subject knowledge by working through clear and focused content coverage- Test understanding and identify areas for improvement with regular Now Test Yourself tasks and answers- Improve exam technique through practice questions and expert tips- Get exam ready with answers to the practice questions available online

Objev podobné jako My Revision Notes: CCEA GCSE Learning for Life and Work: Second Edition - Joanne McDonnell

Written by more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets, and explores connections with data science and more traditional approaches. This is an invaluable resource for researchers and graduate students in financial engineering, as well as practitioners in the sector.

Objev podobné jako Machine Learning and Data Sciences for Financial Markets

This book will help you to prepare for and excel in your nursing associate practice placements. Covering all settings and all fields of nursing, the book will show you how to make the most of each placement and transfer learning and skills from one area of practice to another. Key features:Â Â Â Â Â - Fully mapped to the NMC Standards of Proficiency for Nursing Associates (2018) - Helps you prepare for placements in a way that best supports your professional development and personal wellbeing - Case studies and activities introduce you to different settings across all fields of nursing - Focussed specifically on the requirements of the nursing associate role, helping you to develop into a confident professional practitioner THE SERIES: The Understanding Nursing Associate Practice series (UNAP) is a new collection of books uniquely designed to support trainee nursing associates throughout their training and into a professional career.

Objev podobné jako Practice-Based Learning for Nursing Associates

Look great, smell wonderful, and feel good as you make your own homemade natural body care products. In this Storey BASICS® guide to holistic beauty, Rosemary Gladstar shares more than 30 simple recipes for cleansers, moisturizers, and creams comprised of essential oils and herbs. Make unique shampoos for lustrous, fragrant hair and learn Gladstar⠙s amazing five-step system for glowing skin. Skip the harsh commercial ingredients and use nature⠙s closet to keep your body healthy and beautiful.

Objev podobné jako Herbs for Natural Beauty - Rosemary Gladstar

With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You ll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.

Objev podobné jako Scaling Graph Learning for the Enterprise - Ahmed Menshawy, Sameh Mohamed, Maraim Rizk Masoud

This book covers crucial knowledge and skill sets for developing language tests and setting assessments. Aimed at practitioners of applied linguistics and TESOL and complete with online resources in a companion website for students and lecturers, the book:- covers the theoretical and methodological framework and rationale for language test use, assessment techniques in different language skills and basic strategies for analysis of test quality- stresses the importance of test reliability, validity and fairness in language assessment- provides its audience with theoretical and practical knowledge in language assessment- forms an accessible gateway into the often intimidating world of language assessment, and a unique opportunity for the readers to ground their knowledge of principles in language assessment research.This book equips readers with the ability to use theories and principles in language testing and assessment to design and use language tests and assessments optimally, given the available time and resources in a given context, and promote good practice and research in language testing and assessment.

Objev podobné jako Language Testing and Assessment - Aek Phakiti

Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimisation for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimisation using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the trade-offs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.

Objev podobné jako Machine Learning Algorithms in Depth - Vadim Smolyakov

Kniha - autor Frank Perry, 400 stran, anglicky, brožovaná bez přebalu matná An entertaining and stimulating deep dive for anyone who has ever contemplated not just why we speak the way we do, but why we speak at all. The book is divided into three parts - language, origin and origin of language - and is packed with colourful examples. Part 1 - Language as an isolated phenomenon - How do languages develop, what are the differences and similarities between the languages spoken around the world? Chapters on the origin and use of grammar. Part 2 - Evolution of man - Intellectual development (from Neanderthal to social guru), but also physiological changes in evolution (e.g. brain capacity compared to apes) that have a bearing on our ability to develop language. Nature versus nurture is also discussed, to what extent is language development dependent on genes and environment? Both play a role, but perhaps have less impact than initially you think. Part 3 - Origin...

Objev podobné jako The Dawn of Language: Axes, lies, midwifery and how we came to talk (1529411408)

Wella Professionals Nutricurls Deep Treatment for Waves amp; Curls maska na vlnité a kudrnaté vlasy, které hydratuje, vyživuje a regeneruje.

Objev podobné jako Wella Professionals Nutricurls Deep Treatment for Waves

Build confidence in a range of key textual analysis techniques and skills with this practical companion, full of advice and guidance from experienced experts.- Build analysis techniques and skills through a range of strategies, serving as a useful companion throughout the course - from critical-thinking, referencing and citation and the development of a line of inquiry to reflecting on the writing process and constructing essays for Paper 1 and Paper 2- Develop skills in how to approach a text using textual analysis strategies and critical theory, for both unseen texts (the basis of Paper 1) and texts studied in class- Concise, clear explanations help students navigate the IB requirements, including advice on assessment objectives and how literary and textual analysis weaves through Paper 1, Paper 2, the HL Essay, Individual Oral and the Learner Profile- Build understanding in how to approach texts so that students can write convincingly and passionately about texts through active reading, note-taking, asking questions, and developing a personal response to texts - Engaging activities are provided to test understanding of each topic and develop skills for the exam - guiding answers are available to check your responses

Objev podobné jako Textual analysis for English Language and Literature for the IB Diploma - Angela Stancar Johnson, Carolyn P. Henly

Use LLMs to build better business software applications Autonomously communicate with users and optimize business tasks with applications built to make the interaction between humans and computers smooth and natural. Artificial Intelligence expert Francesco Esposito illustrates several scenarios for which a LLM is effective: crafting sophisticated business solutions, shortening the gap between humans and software-equipped machines, and building powerful reasoning engines. Insight into prompting and conversational programmingâ ”with specific techniques for patterns and frameworksâ ”unlock how natural language can also lead to a new, advanced approach to coding. Concrete end-to-end demonstrations (featuring Python and ASP.NET Core) showcase versatile patterns of interaction between existing processes, APIs, data, and human input. Artificial Intelligence expert Francesco Esposito helps you: Understand the history of large language models and conversational programming Apply prompting as a new way of coding Learn core prompting techniques and fundamental use-cases Engineer advanced prompts, including connecting LLMs to data and function calling to build reasoning engines Use natural language in code to define workflows and orchestrate existing APIs Master external LLM frameworks Evaluate responsible AI security, privacy, and accuracy concerns Explore the AI regulatory landscape Build and implement a personal assistant Apply a retrieval augmented generation (RAG) pattern to formulate responses based on a knowledge base Construct a conversational user interface For IT Professionals and Consultants For software professionals, architects, lead developers, programmers, and Machine Learning enthusiasts For anyone else interested in natural language processing or real-world applications of human-like language in software

Objev podobné jako Programming Large Language Models with Azure Open AI - Francesco Esposito

An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompanimentÂ

Objev podobné jako Probabilistic Machine Learning - Kevin P. Murphy

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Objev podobné jako An Introduction to Statistical Learning - Trevor Hastie, Robert Tibshirani, Daniela Witten, Gareth James

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARLÂ’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the fieldÂ’s foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play. The book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are self-contained and easy to read. Technical content is explained in easy-to-understand language and illustrated with extensive examples, illuminating MARL for newcomers while offering high-level insights for more advanced readers. First textbook to introduce the foundations and applications of MARL, written by experts in the fieldIntegrates reinforcement learning, deep learning, and game theoryPractical focus covers considerations for running experiments and describes environments for testing MARL algorithmsExplains complex concepts in clear and simple languageClassroom-tested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics Resources include code and slidesÂ

Objev podobné jako Multi-Agent Reinforcement Learning - Filippos Christianos, Stefano V. Albrecht

This rich and comprehensive guide to natural dyeing processes will take your practise to the next level.Author, Heidi Iverson, explains how to boost the spectrum of colors you can achieve through using a variety of tannins, mordants, modifiers, and mixing colors to get incredible results naturally.The Natural Dye Handbook includes:• A library of more than 60 plants including fungi, leaves, bark, roots, flowers, fruit, nuts, and seeds, and the incredible natural-dyed rainbow you can create with them.• A back-to-basics look at the fundamental principles of natural dyeing to help you understand each plant and process as you continue on your natural dye journey.• A large section exploring colour theory where Heidi explains how to create your own colour library and techniques for mixing colours.Heidi explores two different approaches to natural dyeing; a traditional style and a more intuitive approach. Discover mindful dye practices, low-energy alternatives and water-conscious methods as well more traditional dye methods using raw dyestuff, whole plants and ground plants. There is also advice about foraging dye plants safely, ethical harvesting and growing your own dye plants.Learn how to create your own dye journal in order to track your progress, in this ultimate handbook for natural dyers looking to take their practise further.

Objev podobné jako The Natural Dye Handbook - Heidi Iverson

This is a practical guide to the use of technology enhanced learning (TEL) in the classroom. Introducing 50 ways to use technology for learning. Areas covered include:- Gamified learning- Social media- Video streaming- The flipped classroom - Instant feedback tools- And many more.  Guidance on how to use these technologies for learning is complemented by an exploration of their impact on learning. For each example, the opportunities for evidencing progress are evaluated.

Objev podobné jako 50 Ways to Use Technology Enhanced Learning in the Classroom - Peter Atherton

Discover just how easy it is to learn German with this updated edition of The Everything Learning German Book with new online audio so you can quickly access the pronunciation guide and exercises while you’re reading.It’s easy to become intimidated by the prospect of learning a foreign language. Now, with online audio, The Everything Learning German Book, 3rd Edition eliminates the stumbling blocks of learning a language to bring you quick and easy success. Whether you are a first-time learner, relearner, or international traveler, you’ll learn the German language through step-by-step instructions and practical exercises. Cultural information about Germany and the German people makes the guide both simple and entertaining. The pronunciation, parts of speech, and basic vocabulary tips covered in this guide will benefit students, travelers, restaurant-goers, and anyone seeking to learn the language upon which much of English is based. You will learn how to: -Understand verbal etiquette -Order in a restaurant -Ask directions -Communicate efficiently while traveling -Greet strangers properly This edition also includes access to online audio, with pronunciation guides and vocabulary lists. Supplemented by both English-to-German and German-to-English dictionaries, this valuable language reference is the perfect way to learn—or relearn—the language.

Objev podobné jako The Everything Learning German Book, 3rd Edition - Edward Swick

This is your one-stop shop to five European languages. With over 6,500 illustrated words and phrases in English, French, German, Spanish, and Italian, and now with a free audio app featuring all these languages, this learner dictionary offers a quick and stimulating way to learn and recall everyday vocabulary. Featuring a wide range of objects and scenes from everyday life, this dictionary shows you what others only tell you. Perfect for tourists and business travellers alike, DK s 5 Language Visual Dictionary is your essential companion when buying food and clothes, talking about work and interests, discussing health and sport, and studying these languages.The dictionary is incredibly easy to follow, with thematically organized vocabulary so you can find closely related words according to topic. Words and phrases are pictured with full-colour photographs and illustrations, helping to fix new vocabulary in your mind. Five comprehensive indexes provide an instant reference point for each language.The supporting audio app enables you to hear each word and phrase spoken out loud by native speakers of English, French, German, Spanish, and Italian. Available on the App Store and Google Play, the audio app is easy to use and provides an intuitive reference for language learning, helping you learn, retain, and pronounce important vocabulary, and make yourself understood.

Objev podobné jako 5 Language Visual Dictionary - DK

A Le Creuset Classic 21 cm magas, Deep Teal színű borsómalom beállítható keramikus őrlő mechanikával rendelkezik. Az ABS műanyagból készült, strapabíró kivitelű termék nagy űrtartalmú tartállyal és ergonomikus formatervezéssel készült. Elegáns megjelenése dekoratív konyhai kiegészítővé teszi.

• Beállítható keramikus őrlő mechanika finom vagy durva őrléshez
• Erős ABS műanyag konstrukció és nagy űrtartalmú tartály
• Ergonomikus design kényelmes kezeléshez
• Elegáns Deep Teal szín, amely dekoratív elem a konyhában

Objev podobné jako Mlýnek na pepř CLASSIC 21 cm, DEEP TEAL, plast, Le Creuset