deep learning joanne quinn michael fullan joanne j mceachen

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

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

Be bold. Think big. Change Schools.Everyday we’re problem solving—but deep down we know the system is no longer working. Do we just keep trying harder, or do we dare to transform how we "do school?"In The Drivers: Transforming Learning for Students, Schools, and Systems, renowned authors Michael Fullan and Joanne Quinn build on their previous books and lay out a complete model for transforming teaching and learning. The goal: making sure students are actually prepared to live and thrive in the complex world around them. Learn to: Orient students, staff, and community around the four drivers: wellbeing and learning, social intelligence, equality investments, systemness Believe that young people can generate more l magic in the world if we enable them Take cues from five school communities who have successfully embarked on system change Lead, develop, and connect with others at different stages of system changeSchools are not meant to be solely feeders to MBAs, Ph.Ds, or corporate boardrooms. When we see schools as local hubs of innovation, and model for students what it means to be community-focused, we will renew the relevancy of our most valuable institutions.

Objev podobné jako The Drivers - Joanne Quinn, Michael Fullan

It s common wisdom that successful organizations adjust quickly and intelligently to shifts in consumer tastes, political climate, and economic opportunity. Less understood is how they do it. In The Six Secrets of Change, Michael Fullan lays out key factors that allow an organization to sustain meaningful change.

Objev podobné jako The Six Secrets of Change - Michael Fullan

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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

When numbers become people, learners thrive Waves of data—indigestible, dehumanized, and disaggregated—are crashing into the education system every day, driving you to distraction. But imagine a world where you’re not being drowned by data, but inspired by it; where that data has a FACE and gives you focused information on how to reach every student. Sharratt and Fullan turn worldwide research into a road map for school leaders to use ongoing assessment to inform instruction and drive equity at the classroom, school, district, and state levels. Inside you will find A fresh look at data to incorporate new learning Updated case studies, figures, and vignettes Insights from more than 500 educators in answering the 3 research questions: Why do we put FACES on data? How do we put FACES on data? and What are the top three leadership skills needed to do this work? An integrated approach to using the 14 Parameters to enhance Deep Learning and critical thinking Tools for committing to "equity and excellence" FACES is about setting up the conditions for success in every classroom: identifying the right factors, at the right time, with the right resources. Its focus on student-centered data will help you: Increase learners’ ... Unknown localization key: "more"

Objev podobné jako Putting FACES on the Data - Lyn D. Sharratt, Michael Fullan

If you''ve been curious about artificial intelligence and machine learning but didn''t know where to start, this is the book you''ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning, 2nd Edition teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math - the book will cover the rest. After an introduction to Python, you''ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models'' performance. You''ll also learn: How to use classic machine learning models like k-Nearest Neighbours, Random Forests, and Support Vector Machines, How neural networks work and how they''re trained, How to use convolutional neural networks, How to develop a successful deep learning model from scratch. You''ll conduct experiments along the way, building to a final case study that incorporates everything you''ve learned. This second edition is thoroughly revised and updated, and adds ... Unknown localization key: "more"

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. • Foundational reinforcement learning concepts and methods • The most popular deep reinforcement learning agents solving high-dimensional environments • Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for ... Unknown localization key: "more"

Objev podobné jako Grokking Deep Reinforcement Learning - Miguel Morales

Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, (NLP) expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key features An overview of NLP and deep learning • Models for textual similarity • Deep memory-based NLP • Semantic role labeling • Sequential NLP Audience For those with intermediate Python skills and general knowledge of NLP. No hands-on experience with Keras or deep learning toolkits is required. About the technology Natural language processing is the science of teaching computers to interpret and process human language. Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learning Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. He’s the technical coordinator of two large European Union-funded research security-related ... Unknown localization key: "more"

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

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

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 success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today''s exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience.After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus ... Unknown localization key: "more"

Objev podobné jako Learning Deep Learning - Magnus Ekman

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

Objev podobné jako The Science of Deep Learning - Iddo Drori

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

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 ... Unknown localization key: "more"

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

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

Objev podobné jako Understanding Deep Learning - Simon J.D. Prince

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

Objev podobné jako Deep Reinforcement Learning - Aske Plaat

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

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

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

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

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

Practical resources for building cohesive governance teams As a supplement to the best-selling The Governance Core, this practical guide will help trustees and superintendents adopt a governance mindset and partnership that creates coherence throughout the district. With a systems thinking approach, the authors provide readers with the strategies and tools needed to build cohesive teams and engage in deeper learning and decision making. The Taking Action Guide for the Governance Core offers readers:  • a deeper understanding of core governance and how to build it • a planning guide to help new trustees get started • protocols and sample agendas for focusing on strategy and systems during open board meetings Educational leaders will find this guide offers them a foundation for building strong, flourishing school districts that are equipped to adapt to and meet the daunting challenges of our time. Â

Objev podobné jako The Taking Action Guide for the Governance Core - Michael Fullan, Babs Kavanaugh, Eleanor Adam, Davis W. -Davis, USA) Campbell

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

Objev podobné jako System Design for Epidemics Using Machine Learning and Deep Learning

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

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

Dětský kostým Harley Quinn je určen pro věkovou skupinu 10-12 let a výšku 140-152 cm. Sada obsahuje triko, legíny s imitací denim šortek, masku na oči a pásek. Kostým je vyroben ze 100% polyesteru a je vhodný pro karnevaly i domácí hraní.

• Kompletní sada obsahující triko, legíny, masku a pásek
• Vhodné pro karnevaly, Halloween i domácí hraní
• Vyrobeno z pohodlného a snadno udržovatelného polyesteru
• Design inspirovaný populární komiksovou a filmovou postavou

Objev podobné jako Epee Dětský kostým Harley Quinn 10-12 let

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

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

Objev podobné jako Inspiring Deep Learning with Metacognition - Nathan Burns

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

Objev podobné jako Deep Learning - Andrew Glassner

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

Objev podobné jako Deep Learning for Cognitive Computing Systems

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

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

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

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

This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project.

Objev podobné jako Deep Learning at Scale - Suneeta Mall

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

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

This captivating and charming novel from international multi-million copy seller Joanne Harris takes us back to the French village we first discovered in Chocolat. Seamlessly interweaving the past and the present, magic and memory, it is a sensual rollercoaster that will appeal to fans of Victoria Hislop, Fiona Valpy, Maggie O''Farrell and Rachel Joyce.''Thickly sensuous, wildly indulgent, magical escapism: Chocolat lovers will drink deeply'' --GUARDIAN''Joanne Harris has the gift of conveying her delight in the sensuous pleasures of food, wine, scent and plants... Blackberry Wine has all the appeal of a velvety scented glass of vintage wine'' -- DAILY MAIL''A wonderful story'' -- ***** Reader review''A beautiful story... beautifully written and very atmospheric'' ***** Reader review''I could NOT put this book down'' ***** Reader review''A very good book, lots of warmth and light'' ***** Reader review*******************************************************************Jay Mackintosh is trapped by memory in the old familiar landscape of his childhood, to which he longs to return.A bottle of home-brewed wine left to him by a long-vanished friend seems to provide the key to an old mystery. As the unusual properties of the strange brew take effect, Jay escapes to a derelict farmhouse in the French village of Lansquenet.There, a ghost from ... Unknown localization key: "more"

Objev podobné jako Blackberry Wine - Joanne Harrisová

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

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

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

Objev podobné jako Machine Learning in Elixir - Sean Moriarity

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

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

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 ... Unknown localization key: "more"

Objev podobné jako Machine Learning Algorithms in Depth - Vadim Smolyakov

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

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

TUBBZ Michael Myers je 5,5 cm vysoká sběratelská PVC figurka v podobě kachničky. Představuje ikonickou postavu z hororové série Halloween s charakteristickými detaily. Figurka není určena pro použití ve vodě.

• Autentický design s ikonickým nožem a chladným pohledem
• Kompaktní velikost 5,5 cm pro snadné vystavení
• Vyrobeno z kvalitního PVC pro dlouhou životnost
• Ideální doplněk pro sběratele hororové tematiky

Objev podobné jako Tubbz kachnička malá Halloween - Michael Myers

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

Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you''ll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results.

Objev podobné jako Ensemble Methods for Machine Learning - Gautam Kunapuli

This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant ... Unknown localization key: "more"

Objev podobné jako Machine Learning for Text - Charu C. Aggarwal

"Simplified Machine Learning" is a comprehensive guide that navigates readers through the intricate landscape of Machine Learning, offering a balanced blend of theory, algorithms, and practical applications. The first section introduces foundational concepts such as supervised and unsupervised learning, regression, classification, clustering, and feature engineering, providing a solid base in Machine Learning theory. The second section explores algorithms like decision trees, support vector machines, and neural networks, explaining their functions, strengths, and limitations, with a special focus on deep learning, reinforcement learning, and ensemble methods. The book also covers essential topics like model evaluation, hyperparameter tuning, and model interpretability. The final section transitions from theory to practice, equipping readers with hands-on experience in deploying models, building scalable systems, and understanding ethical considerations.

Objev podobné jako Simplified Machine Learning - Pooja Sharma

Tubbz kachnička představuje Michaela Myerse z filmové série Halloween v humorném pojetí. Figurka je vyrobena z vinylu, měří 9 cm a je oficiálně licencována. Není určena pro použití ve vodě a je vhodná pro děti od 6 let.

• Oficiálně licencovaná figurka s autentickým designem
• Detailní zpracování charakteristických rysů postavy
• První edice s vysokou sběratelskou hodnotou
• Vyrobeno z kvalitního vinylového materiálu

Objev podobné jako Tubbz kachnička Halloween Michael Myers (první edice)

Michael Kors Pour Homme je parfémovaná voda pro muže s osvěžující citrusovou vůní doplněnou dřevitými a kořenitými tóny. Formát 30 ml je vhodný pro každodenní nošení. Vůně podporuje sebevědomí a je ideální pro aktivní muže.

• Svěží citrusová vůně osvěžující mysl
• Elegantní kombinace dřevitých a kořenitých tónů
• Vhodná pro každodenní nošení a dobrodružství
• Luxusní design a kvalitní zpracování

Objev podobné jako Michael Kors Pour Homme parfémovaná voda pro muže 30 ml

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today''s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available ... Unknown localization key: "more"

Objev podobné jako Machine Learning - Kevin P. Murphy

Skládací skříň Wenko Deep Black o rozměrech 160 × 50 × 75 cm nabízí úložný prostor s policí a šatní tyčí. Je vyrobena z pevné kovové konstrukce s omyvatelným potahem a umožňuje snadnou montáž bez nářadí. Po složení šetří místo, což ji činí vhodnou pro menší byty nebo dočasné využití.

• Snadná montáž bez nářadí a možnost složení pro úsporu místa.
• Stabilní kovová konstrukce s omyvatelným potahem PEVA chránícím před prachem.
• Univerzální využití díky šatní tyči a horní polici v elegantním černém designu.

Objev podobné jako Wenko Skládací skříň z folie PEVA s policí Deep Black, 160 x 50 x 75 cm

In Visible Learning: Lesson Planning, John Hattie and Klaus Zierer make explicit how to implement the world-famous Visible Learning® research into the bedrock of teaching and preparation – lesson planning.By implementing the Visible Learning® data in everyday teaching, this book provides a practical guide to lesson planning that is unique and objective. Important planning steps are explained and described using example lessons in several different subjects. Success criteria are described, and simple strategies to implement, intervene with, and evaluate lessons effectively are provided including, critically, how to switch from surface to deep learning and back again. This book:combines the largest body of empirical educational research to date (now informed by more than 2,100 meta-analyses and implementation in thousands of classrooms globally) with the everyday task of lesson planningincludes empirical research on teaching and learning as well as theoretical studies on lesson planningis orientated toward the phases of analysis, planning, implementation, and evaluation of a lessonillustrates theoretical principles and empirical research results using a specific lessonprovides advice for learners, parents, school administrators, and teachersoffers numerous opportunities for consolidation through in-depth tasks at the levels of surface understanding and deep understandingfollows evidence-based criteria for the successful professionalization of teachersThis powerful and essential ... Unknown localization key: "more"

Objev podobné jako Visible Learning: Lesson Planning - John Hattie, Klaus Zierer

Bezdrátový zvonek Solight se snadno instaluje do zásuvky s dosahem signálu až 180 metrů. Nabízí 36 melodií, 4 úrovně hlasitosti a světelnou LED signalizaci. Funkce Learning code umožňuje spárování více zařízení a tlačítko je vodotěsné s ochranou IP44.

• Snadná instalace bez vrtání – přijímač do zásuvky, tlačítko na dveře nebo zeď.
• Výběr z 36 melodií a 4 úrovní hlasitosti (25–90 dB) s velkým osvětleným LED rámečkem.
• Funkce Learning code umožňuje spárování až 8 tlačítek k jednomu zvonku nebo 15 zvonků k jednomu tlačítku.
• Vodotěsné tlačítko (IP44) s krytem na jmenovku a dosahem až 180 m na otevřeném prostranství.

Objev podobné jako Solight bezdrátový zvonek, do zásuvky, 180m, černý, learning code 1L76B

Rembrandt van Rijn (1606-69) transcends any period or social milieu: he is one of the world’s great masters and though his works reflect the confidence of newly independent Holland, his vision extends far beyond these narrow confines. A deeply perceptive artist (his many self-portraits show his continued interest in the study of human nature), he sought to go beyond superficialities, to endow his biblical paintings, historical narratives, genre scenes and portraits with psychological depths hitherto unknown in Dutch painting. Impatient with conventionally stiffly posed group portraits, he produced such masterpieces as The Night Watch, The Anatomy Lesson of Dr Tulp and the Staal Meesters, while his studies of Saskia, his wife, and his mistress Hendrickje Stoffels reveal his deeply sensuous, compassionate nature.Michael Kitson has revised his highly successful book in the light of the most recent scholarship on Rembrandt, making this the ideal survey of the career of a much-loved genius.

Objev podobné jako Rembrandt - Michael Kitson

Jeremy Bentham – philosopher, theorist of law and of the art of government – was among the most influential figures of the early nineteenth century, and the approach he pioneered – utilitarianism – remains central to the modern world. In this new introduction to his ideas, Michael Quinn shows how Bentham sought to be an engineer or architect of choices and to illuminate the methods of influencing human conduct to good ends, by focusing on how people react to the various physical, legal, institutional, normative and cultural factors that confront them as decision-makers. Quinn examines how Bentham adopted utility as the critical standard for the development and evaluation of government and public policy, and explains how he sought to apply this principle to a range of areas, from penal law to democratic reform, before concluding with an assessment of his contemporary relevance. He argues that Bentham simultaneously sought both to facilitate the implementation of governmental will and to expose misrule by rendering all exercises of public power transparent to the public on whose behalf it was exercised. This book will be essential reading for any student or scholar of Bentham, as well as those interested in the history of political ... Unknown localization key: "more"

Objev podobné jako Bentham - Michael Quinn

In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence. Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and most accessible way to get started with DRL. The authors teach through practical hands-on examples presented with their advanced OpenAI Lab framework. While providing a solid theoretical overview, they emphasize building intuition for the theory, rather than a deep mathematical treatment of results. Coverage includes: Components of an RL system, including environment and agents Value-based algorithms: SARSA, Q-learning and extensions, offline learning Policy-based algorithms: REINFORCE and extensions; comparisons with value-based techniques Combined methods: Actor-Critic and extensions; scalability through async methods Agent evaluation Advanced and experimental techniques, and more How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning Reduces the learning curve by relying on the authors’ OpenAI Lab framework: requires less upfront theory, math, and programming expertise Provides ... Unknown localization key: "more"

Objev podobné jako Foundations of Deep Reinforcement Learning - Laura Graesser, Wah Loon Keng