deep learning with python francois chollet

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

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

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

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

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

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

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

Create simple, easy programs in the popular Python language Beginning Programming with Python For Dummies is the trusted way to learn the foundations of programming using the Python programming language. Python is one of the top-ranked languages, and thereâ ™s no better way to get started in computer programming than this friendly guide. Youâ ™ll learn the basics of coding and the process of creating simple, fun programs right away. This updated edition features new chapters, including coverage of Google Colab, plus expanded information on functions and objects, and new examples and graphics that are relevant to todayâ ™s beginning coders. Dummies helps you discover the wealth of things you can achieve with Python. Employ an online coding environment to avoid installation woes and code anywhere, any time Learn the basics of programming using the popular Python language Create easy, fun projects to show off your new coding chops Fix errors in your code and use Python with external data sets Beginning Programming with Python For Dummies will get new programmers startedâ ”the easy way.Â

Objev podobné jako Beginning Programming with Python For Dummies - John Paul Mueller

Wrangle stats as you learn how to graph, analyze, and interpret data with Python Statistical Analysis with Python For Dummies introduces you to the tool of choice for digging deep into data to inform business decisions. Even if you re new to coding, this book unlocks the magic of Python and shows you how to apply it to statistical analysis tasks. You ll learn to set up a coding environment and use Python s libraries and functions to mine data for correlations and test hypotheses. You ll also get a crash course in the concepts of probability, including graphing and explaining your results. Part coding book, part stats class, part business analyst guide, this book is ideal for anyone tasked with squeezing insight from data. Get clear explanations of the basics of statistics and data analysisLearn how to summarize and analyze data with Python, step by stepImprove business decisions with objective evidence and analysisExplore hypothesis testing, regression analysis, and prediction techniques This is the perfect introduction to Python for students, professionals, and the stat-curious.

Objev podobné jako Statistical Analysis with Python For Dummies - Joseph Schmuller

Learn to code like a professional with Python â “ an open source, versatile, and powerful programming languageKey FeaturesLearn the fundamentals of programming with Python â “ one of the best languages ever createdDevelop a strong set of programming skills that you will be able to express in any situation, on every platform, thanks to Pythonâ ™s portabilityCreate outstanding applications of all kind, from websites to scripting, and from GUIs to data scienceBook DescriptionLearning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application. The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned. What you will learnGet Python up and running on Windows, Mac, and Linux in no timeGrasp the fundamental concepts of coding, along with the basics of data structures and control flow. Write elegant, reusable, and efficient code in any situationUnderstand when to use the functional or the object oriented programming approachCreate bulletproof, reliable software by writing tests to support your codeExplore examples of GUIs, scripting, data science and web applicationsLearn to be independent, capable of fetching any resource you need, as well as dig deeperWho this book is forPython is the most popular introductory teaching language in U.S. top computer science universities, so if you are new to software development, or maybe you have little experience, and would like to start off on the right foot, then this language and this book are what you need. Its amazing design and portability will help you become productive regardless of the environment you choose to work with.

Objev podobné jako Learning Python - Fabrizio Romano

If you ve ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. But what if you could have your computer do them for you? In this fully revised edition of the best-selling classic Automate the Boring Stuff with Python, you ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand - no prior programming experience required. You ll learn the basics of coding in Python and explore the language s rich library of modules for performing specific tasks, like scraping data from websites, searching for text across multiple files, and merging, watermarking, or encrypting PDFs. The third edition includes: Expanded coverage of developer techniques, like creating command line programs; Updated examples and new projects; Additional chapters about working with SQLite databases, speech-recognition technology, video and audio editing, and text-to-speech capabilities; Simplified explanations (based on reader feedback) of beginner programming concepts, like loops and conditionals. Even if you ve never written a line of code, Automate the Boring Stuff with Python, 3rd Edition will teach you how to make your computer take on tedious tasks and do all your grunt work - the way it should be!

Objev podobné jako Automate the Boring Stuff with Python, 3rd Edition - Al Sweigart

A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learningExpansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learningPractical AI and Python ⠜cheat sheet⠝ quick referencesThis hands-on AI programming guide is perfect for anyone with a basic knowledge of programming⠔including familiarity with variables, arrays, loops, if-else statements, and file input and output⠔who seeks to understand foundational concepts in AI and AI development.

Objev podobné jako Artificial Intelligence Programming with Python - Perry Xiao

Al Sweigart s classic coding book Automate the Boring Stuff with Python, now in its third edition, has taught more than half a million readers how to dispense with tedious tasks using the Python programming language. In this hands-on companion workbook, Sweigart gives those readers - and any Python programming beginner - hundreds of new ways to practice what they ve learned. Through a wide variety of exercises, readers will test their understanding of programming concepts, face down tricky challenges, and explore use cases of common techniques. The workbook s creative projects encourage readers to build games, animations, and digital tools, offering novel ways to think about Python s applications in their everyday life. Covers Python 3.x and its ecosystem of third-party libraries.

Objev podobné jako Automate the Boring Stuff with Python Workbook - Al Sweigart

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

Coding with Python â “ Create Amazing Graphics introduces coding in Python through a variety of projects. Each one teaches new coding concepts and results in some amazing graphics.Python is a powerful, text-based programming language essential to grasp for serious coding but can be dull to learn. This book focuses on inspired learning. Step-by-step, it illustrates how to use Python code to create exciting and colourful graphics â ” making learning Python great fun!Learn Python code to:Use random numbers to create unique artworkMix colours together using variables to create amazing effectsUse loops to repeat your code and create intricate patternsCode your own functions and build up your own designs

Objev podobné jako Coding with Python - Create Amazing Graphics - Max Wainewright

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

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

Humans learn best from feedbackâ ”we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques youâ ™ll need to implement it into your own projects. Key features â ¢ Structuring problems as Markov Decision Processes â ¢ Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them â ¢ Applying reinforcement learning algorithms to real-world problems Audience Youâ ™ll need intermediate Python skills and a basic understanding of deep learning. About the technology Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously contributed to the success of AlphaGo but thatâ ™s not all it can do! Alexander Zai is a Machine Learning Engineer at Amazon AI working on MXNet that powers a suite of AWS machine learning products. Brandon Brown is a Machine Learning and Data Analysis blogger at outlace.com committed to providing clear teaching on difficult topics for newcomers.

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

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

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

The mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you ll peer inside the ⠜black box⠝ to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. about the technology It s important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You ll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you ll be glad you can quickly identify and fix problems. about the book Math and Architectures of Deep Learning sets out the foundations of DL in a way that s both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You ll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you re done, you ll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge.

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

Learning to code your own shoot- em-up is infinitely more satisfying than beating any end-of-level bossWhile countless millions of us like nothing more than spending hours racking up high scores on our favourite games, too few of us are exposed to an even more gratifying way to spend the evening - making them. It s far from easy. Master essential game-making skills:Creating shapes and pathsMovement and animationUsing the keyboard and mouseAdding sound and musicSimulating physics and forcesBuilding classes for actorsCreating your own shoot-em upEach chapter will add to your knowledge of Python game development, allowing you both to understand the games you play, and to create almost anything your imagination can come up with.This book is designed to help you learn many of the essential skills you ll need to make games with Python and Pygame, and you ll learn valuable coding skills along the way.This book isn t for absolute programming beginners, but it s not far from it. If you ve written some simple Python (or similar) programs in the past, and are able to do things like creating files and get around your computer s filesystem without too much difficulty, then you re ready to get started.

Objev podobné jako Make Games with Python - Sean M Tracey

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz s popular training course, this updated sixth edition will help you quickly write efficient, high-quality code with Python.

Objev podobné jako Learning Python - Mark Lutz

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

This series supports learners through the Cambridge IGCSE™ and O Level Computer Science syllabuses (0478/0984/2210). Develop skills and confidence with our programming book for Python. Created to support students undertaking the Cambridge IGCSE™ and O Level Computer Science syllabuses, this resource provides tailored support when programming with Python. A three-tiered approach to programming tasks across the book provides scaffolded support for students of all levels of understanding. Answers are accessible in the Solutions chapter in the digital part of the resource on the Cambridge GO platform, enabling students to practise their programming skills in class or at home.

Objev podobné jako Cambridge IGCSEâ„¢ and O Level Computer Science Programming Book for Python with Digital Access (2 Years) - Chris Roffey

Francoise Gilot was a young painter in Pasis when she first met Picasso - he was sixty-two and she was twenty-one. During the following ten years they were lovers, worked closely together and she became mother to two of his children, Claude and Paloma. Life with Picasso, her account of those extraordinary years, is filled with intimate and astonishing revelations about the man, his work, his thoughts and his friends - Matisse, Braque, Gertrude Stein and Giacometti among others.Francois Gilot paints a compelling portrait of her turbulent life with the temperamental genius that was Picasso. She is a superb witness to Picasso as an artist and to his views on art.

Objev podobné jako Life With Picasso - Francoise Gilot

Python Workout presents 50 exercises designed to deepen the reader⠙s skill with Python. Readers will not only tackle exercises using built-in data structures, but also more advanced techniques, such as functional programming, object-oriented programming, iterators, and generators. With each engaging challenge, readers will practice a new skill and learn how to apply it to everyday coding tasks. Key Features 50 hands-on exercises and solutions Basic Python sequence types Python dictionaries and sets Functional programming in Python Creating your own classes Working with Python objects Generator functions Intended for readers with basic Python skills. About the technology Python is a versatile, elegant, general purpose programming language. Essential for data analysis, web development, artificial intelligence, games, desktop apps, and more, Python skills are a hot commodity. Reuven M. Lerner, an independent consultant for more than two decades, teaches Python, data science, and Git to companies around the world. His Better developers newsletter and blog are read by thousands of Python developers each week. Reuven has written a monthly column, ⠜At the Forge,⠝ for Linux Journal since 1996 and is a panellist on the weekly Freelancers Show podcast. Reuven lives with his wife and three children in Modi⠙in, Israel, and can be reached at https://lerner.co.il/ or on Twitter at @reuvenmlerner.

Objev podobné jako Python Workout - Reuven Lerner

An intuitive guide for professionals wanting to prepare for the future of Microsoft Excel by building Python in Excel skills and unleashing the power of their data. A hands-on guide to the foundational Python in Excel skills youâ ™ll need to understand and use this powerful analytics tool, Python in Excel Step-by-Step is for current Excel users interested in expanding their data analysis skillset with Python. Analytics educator and Microsoft Excel MVP David Langer demonstrates how to use Python in Excel, tounlock new analytics capabilities in Excel, and build your foundation for the future of Excel: do-it-yourself (DIY) data science. The book leverages your existing Excel knowledge to learn the Python foundation you can apply right away. This is the same approach David has used to successfully teach more than 1,000 professionals Python â “ even if youâ ™ve never written code before. David also includes: Targeted coverage of the Python fundamentals required for analytics â “ learn just what you need fastHow to use the powerful pandas and plotnine libraries to facilitate data manipulation and visualization using Python in ExcelA DIY data science roadmap for you to build the skills you need to unleash the power of your data to have more impact at work Perfect for professionals use Microsoft Excel for data analysis, like marketing managers, financial analysts, and supply chain manager, Python in Excel Step-by-Step is an invaluable new resource for all business professionals who use Excel and want to build skills for Excelâ ™s AI-powered future.

Objev podobné jako Python in Excel Step-by-Step - David Langer

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

Streamline Your Workflow and Boost Productivity with Python Automation In today s workplaces, there s a high demand for know-how on the Python programming language, especially for writing time-saving scripts that can simplify routine work tasks. Python Automation For Dummies delivers, with simple explanations of how you can use Python to automatically wrangle data files, manage media files, create shortcuts, find and organize web data, and even analyze social media for trends. With this easy-to-follow Dummies guide, you can upskill, expand your productivity, and speed up the process of generating data-driven insights. You ll even learn to enhance your Python automations with AI, for workflows that are faster and smarter. Review the basics of Python coding and follow steps for automating all sorts of tasksManage large file sets, organize and analyze data, and speed up research processesAutomate scheduling and other time-consuming tasksâ ”and optimize work with AIFree up time and resources by automating routine work, so you can foucs on more important issues This is a great Dummies resource for Python developers interested in applying the popular coding language to make workflows more efficient.

Objev podobné jako Python Automation For Dummies - Alan Simpson

Data science and machine learningâ ”two of the world s hottest fieldsâ ”are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world s #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help students with widely diverse backgrounds learn foundational Python so they can use it for data science and machine learning. This book is catered to introductory-level college courses on data science. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once students have learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and moreâ ”all created with Colab (Jupyter compatible) notebooks, so students can execute all coding examples interactively without installing or configuring any software.

Objev podobné jako Foundational Python for Data Science - Kennedy Behrman

Kniha - autor Eric Matthes, 544 stran, anglicky, brožovaná bez přebalu matná Python Crash Course is the world’s bestselling programming book, with over 1,500,000 copies sold to date! Python Crash Course is the world’s best-selling guide to the Python programming language. This fast-paced, thorough introduction will have you writing programs, solving problems, and developing functioning applications in no time. You’ll start by learning basic programming concepts, such as variables, lists, classes, and loops, and practice writing clean code with exercises for each topic. You’ll also learn how to make your programs interactive and test your code safely before adding it to a project. You’ll put your new knowledge into practice by creating a Space Invaders–inspired arcade game, building a set of data visualizations with Python’s handy libraries, and deploying a simple application online. As you work through the book, you’ll learn how to: Use powerful Python...

Objev podobné jako Python Crash Course: 3rd Edition (9781718502703)

This trusted guide demonstrates the practical advantages of test-driven development (TDD) with Python and describes how to develop a real web application. You ll learn how to write and run tests before building each part of your app and then develop the minimum amount of code required to pass those tests. The result? Clean code that works.

Objev podobné jako Test-Driven Development with Python - Harry Percival

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

Objev podobné jako Generative Deep Learning - David Foster

This 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

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

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

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

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

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

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

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

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

Selected as the Michigan Council of Teachers of Mathematics winter book club book! Rich tasks, collaborative work, number talks, problem-based learning, direct instruction⠦with so many possible approaches, how do we know which ones work the best? In Visible Learning for Mathematics, six acclaimed educators assert it⠙s not about which one⠔it⠙s about when⠔and show you how to design high-impact instruction so all students demonstrate more than a year⠙s worth of mathematics learning for a year spent in school. That⠙s a high bar, but with the amazing K-12 framework here, you choose the right approach at the right time, depending upon where learners are within three phases of learning: surface, deep, and transfer. This results in visible learning because the effect is tangible. The framework is forged out of current research in mathematics combined with John Hattie⠙s synthesis of more than 15 years of education research involving 300 million students. Chapter by chapter, and equipped with video clips, planning tools, rubrics, and templates, you get the inside track on which instructional strategies to use at each phase of the learning cycle: Surface learning phase: When⠔through carefully constructed experiences⠔students explore new concepts and make connections to procedural skills and vocabulary that give shape to developing conceptual understandings. Deep learning phase: When⠔through the solving of rich high-cognitive tasks and rigorous discussion⠔students make connections among conceptual ideas, form mathematical generalizations, and apply and practice procedural skills with fluency. Transfer phase: When students can independently think through more complex mathematics, and can plan, investigate, and elaborate as they apply what they know to new mathematical situations. To equip students for higher-level mathematics learning, we have to be clear about where students are, where they need to go, and what it looks like when they get there. Visible Learning for Math brings about powerful, precision teaching for K-12 through intentionally designed guided, collaborative, and independent learning.

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

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 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

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

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

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

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

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

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

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