machine learning with neural networks bernhard mehlig

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

Objev podobné jako Machine Learning with Neural Networks - Bernhard Mehlig

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions.

Objev podobné jako Introduction to Machine Learning with Python - Andreas C. Mueller

This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.

Objev podobné jako Introduction to Machine Learning with Applications in Information Security - Mark Stamp

This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.

Objev podobné jako Graph-Powered Analytics and Machine Learning with TigerGraph - Ph.D., Victor Lee, Xinyu Chang, Phuc Kien Nguyen

Fans of Chris Ferrie s ABCs of Economics, ABCs of Space, and Organic Chemistry for Babies will love this introduction to neural networks for babies and toddlers!Help your future genius become the smartest baby in the room! It only takes a small spark to ignite a child s mind. Neural Networks for Babies by Chris Ferrie is a colorfully simple introduction to the study of how machines and computing systems are created in a way that was inspired by the biological neural networks in animal and human brains. With scientific and mathematical information from an expert, this installment of the Baby University board book series is the perfect book for enlightening the next generation of geniuses.After all, it s never too early to become a scientist!If you re looking for programming for babies, coding for babies, or more Baby University board books to surprise your little one, look no further! Neural Networks for Babies offers fun early learning for your little scientist!

Objev podobné jako Neural Networks for Babies - Chris Ferrie, Sarah Kaiser

It s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily available machine learning tools! In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. Practical examples illustrate each new concept to ensure you⠙re grokking as you go. You⠙ll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Key Features · Different types of machine learning, including supervised and unsupervised learning · Algorithms for simplifying, classifying, and splitting data · Machine learning packages and tools · Hands-on exercises with fully-explained Python code samples For readers with intermediate programming knowledge in Python or a similar language. About the technology Machine learning is a collection of mathematically-based techniques and algorithms that enable computers to identify patterns and generate predictions from data. This revolutionary data analysis approach is behind everything from recommendation systems to self-driving cars, and is transforming industries from finance to art. Luis G. Serrano has worked as the Head of Content for Artificial Intelligence at Udacity and as a Machine Learning Engineer at Google, where he worked on the YouTube recommendations system. He holds a PhD in mathematics from the University of Michigan, a Bachelor and Masters from the University of Waterloo, and worked as a postdoctoral researcher at the University of Quebec at Montreal. He shares his machine learning expertise on a YouTube channel with over 2 million views and 35 thousand subscribers, and is a frequent speaker at artificial intelligence and data science conferences.

Objev podobné jako Grokking Machine Learning - Luis Serrano

Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor s Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and WekaUnderstand decision trees, Bayesian networks, and artificial neural networksImplement Association Rule, Real Time, and Batch learningDevelop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.

Objev podobné jako Machine Learning - Jason Bell

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

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

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

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

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

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book s web site.

Objev podobné jako Mathematics for Machine Learning - A. Aldo Faisal, Marc Peter Deisenroth, Cheng Soon Ong

Prepares students for Microsoft Exam 70-774-and helps them demonstrate real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT students ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level.

Objev podobné jako Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning - Ginger Grant, Tamanaco Francisquez, Pau Sempere, Paco Gonzalez, Julio Granado

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

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

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

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

Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of â„“1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso.In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

Objev podobné jako Statistical Learning with Sparsity - Martin Wainwright, Trevor Hastie, Robert Tibshirani

With this practical guide, data scientists and ML engineers will learn how to bridge the gap between data science and Lean software delivery in a practical and simple way. David Tan and Ada Leung show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects.

Objev podobné jako Effective Machine Learning Teams - Ada Leung, David Tan, David Colls

A brilliant scientist constructs a machine, which, with the pull of a lever, propels him to the year AD 802,701. Part of the Macmillan Collectorâ ™s Library; a series of stunning, clothbound, pocket-sized classics with gold foiled edges and ribbon markers. These beautiful books make perfect gifts or a treat for any book lover. This edition of The Time Machine features an introduction by Dr Mark Bould.The Time Traveller finds himself in a verdant, seemingly idyllic landscape where he is greeted by the diminutive Eloi people. The Eloi are beautiful but weak and indolent, and the explorer is perplexed by their fear of the dark. He soon discovers the reason for their fear - the Eloi are not the only race to have inherited the earth. When his time machine disappears, the Time Traveller must descend alone into the subterranean tunnels of the Morlocks - a terrifying, carnivorous people who toil in darkness - to reclaim it.

Objev podobné jako The Time Machine - Herbert George Wells

Effective teaching is effective teaching, no matter where it occursThe pandemic teaching of mid-2020 was not really distance learning, but rather crisis teaching. But starting now, teachers have the opportunity to prepare for distance learning with purpose and intent⠔using what works best to accelerate students⠙ learning all the while maintaining an indelible focus on equity.Harnessing the insights and experience of renowned educators Douglas Fisher, Nancy Frey, and John Hattie, The Distance Learning Playbook applies the wisdom and evidence of VISIBLE LEARNING® research to understand what works best with distance learning. Spanning topics from teacher-student relationships, teacher credibility and clarity, instructional design, assessments, and grading, this comprehensive playbook details the research- and evidence-based strategies teachers can mobilize to deliver high- impact learning in an online, virtual, and distributed environment.This powerful guide includes: Learning Intentions and Success Criteria for each module to track your own learning and model evidence-based teacher practices for meaningful learning A diversity of instructional approaches, including direct instruction, peer learning, and independent work that foster student self-regulation and move learning to deep and transfer levels Discussion of equity challenges associated with distance learning, along with examples of how teachers can work to ensure that equity gains that have been realized are not lost. Special guidance for teachers of young children who are learning from a distance Videos of the authors and teachers discussing a wide variety of distance learning topics Space to write and reflect on current practices and plan future instruction The Distance Learning Playbook is the essential hands-on guide to preparing and delivering distance learning experiences that are truly effective and impactful.To purchase from an Authorized Corwin Distributor click here.A Spanish translation of the Distance Learning Playbook, Grades K-12, Aprendizaje a Distancia Guia, Guia de Preescolar a Bachillerator, can be purchased by contacting Irene Yepez from Editorial Trillas at vigaexporta@trillas.mx.

Objev podobné jako The Distance Learning Playbook, Grades K-12 - John Hattie, Douglas Fisher, Nancy Frey

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

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field s key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning s relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson s wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Objev podobné jako Reinforcement Learning - Andrew G. Barto, Richard S. Sutton

On publication in 2009 John Hattieâ ™s Visible Learning presented the biggest ever collection of research into what actually work in schools to improve childrenâ ™s learning. Not what was fashionable, not what political and educational vested interests wanted to champion, but what actually produced the best results in terms of improving learning and educational outcomes. It became an instant bestseller and was described by the TES as revealing educationâ ™s â ˜holy grailâ ™. Now in this latest book, John Hattie has joined forces with cognitive psychologist Greg Yates to build on the original data and legacy of the Visible Learning project, showing how itâ ™s underlying ideas and the cutting edge of cognitive science can form a powerful and complimentary framework for shaping learning in the classroom and beyond.Visible Learning and the Science of How We Learn explains the major principles and strategies of learning, outlining why it can be so hard sometimes, and yet easy on other occasions. Aimed at teachers and students, it is written in an accessible and engaging style and can be read cover to cover, or used on a chapter-by-chapter basis for essay writing or staff development. The book is structured in three parts â “ â ˜learning within classroomsâ ™, â ˜learning foundationsâ ™, which explains the cognitive building blocks of knowledge acquisition and â ˜know thyselfâ ™ which explores, confidence and self-knowledge. It also features extensive interactive appendices containing study guide questions to encourage critical thinking, annotated bibliographic entries with recommendations for further reading, links to relevant websites and YouTube clips. Throughout, the authors draw upon the latest international research into how the learning process works and how to maximise impact on students, covering such topics as:teacher personality;expertise and teacher-student relationships;how knowledge is stored and the impact of cognitive load;thinking fast and thinking slow;the psychology of self-control;the role of conversation at school and at home;invisible gorillas and the IKEA effect;digital native theory;myths and fallacies about how people learn.This fascinating book is aimed at any student, teacher or parent requiring an up-to-date commentary on how research into human learning processes can inform our teaching and what goes on in our schools. It takes a broad sweep through findings stemming mainly from social and cognitive psychology and presents them in a useable format for students and teachers at all levels, from preschool to tertiary training institutes.

Objev podobné jako Visible Learning and the Science of How We Learn - John Hattie, Gregory C. R. Yates

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

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

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

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

Effective school leadership is effective leadership, regardless of where it occurs In March 2020, there was no manual for leading schools and school systems during a pandemic. School leaders had to figure things out as the crisis unfolded. But starting now, leaders have the opportunity to prepare for leading schools through distance learning with purpose and intentâ ”using what works best to accelerate studentsâ ™ learning all the while maintaining an indelible focus on equity. Harnessing the insights and experience of renowned educators Douglas Fisher, Nancy Frey, and John Hattie, The Distance Learning Playbook for School Leaders applies the wisdom and evidence of the VISIBLE LEARNING® research to understand what works best. Spanning topics from school climate at a distance, leader credibility, care for self and colleagues, instructional leadership teams, stakeholder advisory groups, and virtual visibility, this comprehensive playbook details the research- and evidence-based strategies school leaders can mobilize to lead the delivery of high-impact learning in an online, virtual, and distributed environment. This powerful guide includes: Actionable insights and hands-on steps for each module to help school leaders realize the evidence-based leadership practices that result in meaningful learning in a distance environment Discussion of equity challenges associated with distance learning, along with examples of how leaders can work to ensure that equity gains that have been realized are not lost. Analysis of the mindsets that empower leaders to manage change, rather than technology Space to write and reflect on current practices and plan future leadership strategies The mindframes for distance learning that serve leaders well in any instructional setting and will position schools after the pandemic to come back better than they were before The Distance Learning Playbook for School Leaders is the essential hands-on guide to leading school and school systems from a distance and delivering on the promise of equitable, quality learning experiences for students. Â

Objev podobné jako The Distance Learning Playbook for School Leaders - John Hattie, Douglas Fisher, Nancy Frey, Dominique Smith

Help children learn to MACHINE SEW with 30 SUPER-FUN and CREATIVE projects!An all-new collection from BESTSELLING author ANGELA PRESSLEY, expert sewing teacher and star of SEWING STREET TV. Packed full of colorful and unique ideas, this STEP-BY-STEP guide will inspire children to machine-sew their own amazing toys, accessories, decorations, and gifts. Angela Pressley has designed 30 projects she knows kids will love to make, from cuddly mama and baby stringray toys to a cute sausage dog complete with a mini bandana. Customize bedrooms with a colorful striped pillow and wall storage pockets and make unique, on-trend accessories including a bubble tea pencil case and a matching slouchy beanie and mittens. Also included are easy-to-use templates, a guide to the basic sewing kit needed and a handy techniques section with clear step-by-step illustrations. Each of the projects has a skill rating, so children can start with the simplest designs and move on to more challenging projects as they build their skills and confidence.

Objev podobné jako Sewing Machine Projects for Children - Angela Pressley

For the last eight years, James and Kate have been working together to design, implement and evaluate a whole-school, evidence-informed approach to teaching and learning known as Learning Skills. An eight-year study with the University of Cambridge revealed that Learning Skills led to significant gains in subject learning, with rapid gains among students from disadvantaged backgrounds. In this practical guide for teachers and school leaders, James and Kate reveal a recipe for success rooted in three key concepts: metacognition (reflecting on learning); self-regulation (taking ownership over the learning process); and oracy (developing high-quality speaking and listening skills). This is a book about what happened when a small team of teachers seized an opportunity to provide their students with the knowledge, the skills and the confidence to take control of their own learning. This journey began with a question: how and what would we teach, if there was no one watching? On the other side of fear is the teacher you want to be, and the children you d like to teach...

Objev podobné jako Fear Is The Mind Killer: Why Learning to Learn deserves lesson time - and how to make it work for your pupils - James Mannion, Kate McAllister

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

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

Learn to read with your favourite Disney characters!Get your child started on their reading journey with Disney Learning: Starting to Read. Perfect for kids aged 3-5, the fun, simple exercises make reading enjoyable at the preschool level, with review pages to help you track your child⠙s progress.This reading workbook introduces preschool kids to the alphabet and letter sounds, and reinforces this learning with dozens of reading and writing exercises that engage their minds and boost their confidence. Favourite Disney characters will join them every step of the way, inspiring them to get a head start for school!This fun reading workbook for children offers:Curriculum-aligned learning material that⠙s been approved by educational experts, with clear levelling guidance to ensure your child is learning at the correct levelReading and writing exercises including letter recognition, handwriting, letter sounds, rhymes, and upper lowercase lettersParent/carer notes and review pages to provide support on how best to use this book for your childA completion certificate and 50 stickers to reward learners as they progressSo, what are you waiting for? With the charming cast of Disney characters as your guide, learning to read for kids aged 3-5 has never been more fun!© 2025 Disney

Objev podobné jako Disney Learning Starting to Read (Ages 3-5) - DK

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

A stunning little learning library featuring artwork from The Very Hungry Caterpillar.· Introduce first concepts to toddlers· Four mini books: Colours, Animal Sounds, Numbers and Words · Made up of sturdy board book pages· Ideal for rough and tumble toddlersPreschoolers will love learning with the Very Hungry Caterpillar!

Objev podobné jako The Very Hungry Caterpillar: Little Learning Library - Eric Carle

The first English-Tamil Learning Library of 10 board books to develop basic concepts for little scholars. Its well-researched pictures and words ensure faster development of a child s vocabulary and improve observation skills. The topics included in the set are numbers (yengal), colours (nirangal), farm animals and pets (pannai vilangugal chella pranigal), fruits (pazhangal), shapes (vadivangal), vegetables (kaikarigal), birds (paravaigal), wild animals (Kaatu Vilangugal), abc (aangila ezhuthukkal) and Tamil alphabet (Agara Varisai).Preschoolers will love learning with these chunky books!â ¢

Objev podobné jako My First English -Tamil Learning Library

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

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

Be inspired to enhance classroom learning with this textbook, by highly respected and experienced author Louise Burnham.-Build your learning support skills with guidance tailored to the extensive new CACHE qualification due to launch in January 2018-Gain confidence in your role with practical advice and full explanations from best-selling author in STL , Louise Burnham -Translate theory into practice with Tips for Best Practice and Case Studies for challenging topics such as Behaviour Management-Strengthen your understanding of theory and practice, with comprehensive information linked clearly to assessment criteria-Find all the information you need with the colourful, clear design and appropriate language throughout the book -Make the most of your training with the Stretch and Challenge feature-Engage in debate on important STL topics with Classroom Discussion suggestions

Objev podobné jako NCFE CACHE Level 2 Certificate in Supporting Teaching and Learning - Louise Burnham

Introduce your little scholar to the joy of bilingual learning with the First Book of English Gujarati boxed set! Featuring 10 beautifully crafted board books, this collection covers essential topics like ABC, numbers, animals, fruits, and colors. With vibrant illustrations, accurate word labels, and everyday concepts, it s the perfect foundation for building vocabulary in English and Gujarati.Bright Minds Start with Two Languages!Develops vocabulary in both English and Gujarati.Offers accurate word labels and contextually relevant imagesTopics are relatable and encourage real-world learningStrengthens language, observation, and cognitive skillsDurable board books with rounded edges

Objev podobné jako My first English-Gujarati Learning Library - Wonder House Books

Make a difference to classroom learning with this textbook, written for the CACHE qualification by highly respected and experienced author Louise Burnham.-Develop your skills as a teaching assistant with coverage of all units in the new CACHE qualification. -Build confidence in your role with practical advice and full explanations from best-selling author Louise Burnham. -Translate theory into practice with Tips for Best Practice and Case Studies for challenging topics such as Behaviour Management.-Strengthen your understanding of theory and practice, with comprehensive information linked clearly to assessment criteria.-Find all the information you need with the colourful, clear design, and appropriate language throughout. -Make the most of your training with the Stretch and Challenge feature.-Engage in debate on important topics with Classroom Discussion suggestions.

Objev podobné jako NCFE CACHE Level 3 Diploma in Supporting Teaching and Learning - Louise Burnham

Learn to count from one to ten with the help of this bright and engaging book with sequencing and matching activities to reinforce early learning. With gorgeous new illustrations and irresistible cover finishes, this is pocket money power punching well above its weight.

Objev podobné jako Early Learning: 123

Updated to emphasize new features in Spark 2.4., this second edition shows data engineers and scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine-learning algorithms.

Objev podobné jako Learning Spark - Denny Lee, Brooke Wenig, Tathagata Das, Jules Damji

Be prepared for exam day with Barron’s. Trusted content from AP experts! Barron’s AP Computer Science Principles Premium, 2026 includes in-depth content review and online practice. It’s the only book you’ll need to be prepared for exam day.Written by Experienced Educators Learn from Barron’s--all content is written and reviewed by AP experts Build your understanding with comprehensive review tailored to the most recent exam Get a leg up with tips, strategies, and study advice for exam day--it’s like having a trusted tutor by your side Be Confident on Exam Day Sharpen your test‑taking skills with 6 full‑length practice tests–3 in the book, including a diagnostic test to target your studying, and 3 more online–plus detailed answer explanations for all questions Strengthen your knowledge with in‑depth review covering all Big Ideas on the AP Computer Science Principles Exam Reinforce your learning with practice questions at the end of each chapter that cover all frequently tested topics Prepare for the AP Computer Science Principles Create Performance Task with 6 full sample Create Performance Tasks with complete written reports and requirements for scoring Robust Online Practice Continue your practice with 3 full‑length practice tests on Barron’s Online Learning Hub Simulate the exam experience with a timed test option Deepen your understanding with detailed answer explanations and expert advice Gain confidence with scoring to check your learning progress Going forward, this exam will only be offered in a digital format. Barron #39;s AP online tests offer a digital experience with a timed test option to get you ready for test day. Visit the Barron #39;s Learning Hub for more digital practice.Publisher #39;s Note: Products purchased from 3rd party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entities included with the product.

Objev podobné jako AP Computer Science Principles Premium, 2026: Prep Book with 6 Practice Tests + Comprehensive Review + Online Practice - Seth Reichelson

Be prepared for exam day with Barron’s. Trusted content from AP experts!Barron’s AP Computer Science A Premium, 12th Edition includes in‑depth content review and practice. It’s the only book you’ll need to be prepared for exam day. Written by Experienced EducatorsLearn from Barron’s‑‑all content is written and reviewed by AP expertsBuild your understanding with comprehensive review tailored to the most recent examGet a leg up with tips, strategies, and study advice for exam day‑‑it’s like having a trusted tutor by your sideBe Confident on Exam DaySharpen your test‑taking skills with 6 full‑length practice tests–3 in the book, including a diagnostic test to target your studying, and 3 more online–plus detailed answer explanations for all questionsStrengthen your knowledge with in‑depth review covering all units on the AP Computer Science A examReinforce your learning with dozens of clear examples and a series of multiple‑choice practice questions at the end of each review chapterLearn the key techniques and methods of modern programming with a chapter devoted to the Java language features you need to know for test dayRobust Online PracticeContinue your practice with 3 full‑length practice tests on Barron’s Online Learning HubSimulate the exam experience with a timed test optionDeepen your understanding with detailed answer explanations and expert adviceGain confidence with scoring to check your learning progress

Objev podobné jako AP Computer Science A Premium, 12th Edition: Prep Book with 6 Practice Tests + Comprehensive Review + Online Practice - Roselyn Teukolsky

A completely new Upper Intermediate level of the world s most trusted English course, with brand new digital resources bringing you a completely up-to-date blended Headway course. With fully revised texts and topics, refreshed design and artwork, and brand new digital resources, the Upper Intermediate Fourth Edition takes the Headway experience to a new level. Headway Upper Intermediate Fourth edition remains true to its trusted methodology that has worked for millions of students around the world, but delivers it in a revised and up-to-date format. The brand new digital components for teachers and learners – iTools, iTutor and iChecker – make teaching and learning with Headway Fourth edition a truly flexible and interactive experience. Key Features: •New and updated texts and topics •Integrated skills syllabus with a clear grammar focus •iTools – an all-in-one teaching resource for the interactive classroom •iTutor and iChecker – interactive self-study discs that come with the Student s Book and the Workbook •Full teacher support – resources, photocopiables, tests and more - online and in print •Brand new video content, available for students on iTutor with interactive exercises With the Fourth edition of the best-selling adult course book you can now experience the trusted Headway methodology using the latest in classroom technology: •iTools bring the Fourth edition Student s Book and Workbook alive on the classroom wall, complete with interactive exercises, audio, video, and other teaching resources. The built-in tools like zoom, highlight and screen shade give you and your students a truly interactive teaching and learning experience. iTools also includes customizable flipcharts, wordlists and grammar reference to help you make the most of your Headway lessons. •iTutor is the digital companion to the Student s Book that allows students to revise, review and improve their English outside the classroom. To read more about iTutor and how it encourages learner autonomy, see the iTutor tab. •iChecker is a new digital resource, available to students with the Workbook. It contains all Workbook audio and practice tests to help students identify areas where they need more study. To learn more, see the iChecker tab.

Objev podobné jako New Headway Upper Intermediate Student´s Book with iTutor DVD-ROM (4th) - John Soars, Liz Soars