designing machine learning systems chip huyen

In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.

Objev podobné jako Designing Machine Learning Systems - Chip Huyen

In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

Objev podobné jako AI Engineering - Chip Huyen

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

Objev podobné jako Grokking Machine Learning - Luis Serrano

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

Objev podobné jako Machine Learning - Jason Bell

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

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

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

Objev podobné jako Machine Learning Algorithms in Depth - Vadim Smolyakov

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

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

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning—also known as data mining or predictive analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This fourth edition of Machine Learning for Business Analytics also includes: An expanded chapter on deep learningA new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presentedA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

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

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

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

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

Objev podobné jako Probabilistic Machine Learning - Kevin P. Murphy

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

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

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

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

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

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

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

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

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

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

Objev podobné jako Machine Learning and Data Sciences for Financial Markets

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

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

This best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

Objev podobné jako Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e - Géron Aurélien

Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to increasingly complex and data-driven power systems with distributed energy resources to enable evidence-driven decision-making and mitigate catastrophic power shortages. The book reviews foundations towards the integration of machine learning and smart power systems before addressing key challenges and issues. The work then explores AI- and ML-informed techniques to rebalancing of supply and demand. Methods discussed include distributed energy resources and prosumer markets, electricity demand prediction, component fault detection, and load balancing. Security solutions are introduced, along with potential solutions to cyberattacks, security data detection and critical loads in power systems. The work closes with a lengthy discussion, informed by case studies, on integrating AI and ML into the modern energy sector.

Objev podobné jako Smart Energy and Electric Power Systems

"The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd Cook The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher François Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You''ll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable. about the technologyMachine learning has made remarkable progress in recent years. We''ve gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn''t beat a serious Go player, to defeating a world champion. Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sector about the bookDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You''ll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You''ll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you''ll have the knowledge and hands-on skills to apply deep learning in your own projects. what''s insideDeep learning from first principlesImage-classification, imagine segmentation, and object detectionDeep learning for natural language processingTimeseries forecastingNeural style transfer, text generation, and image generation about the readerReaders need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. about the authorFrançois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does AI research, with a focus on abstraction and reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

Objev podobné jako Deep Learning with Python - François Chollet

If you''ve been curious about artificial intelligence and machine learning but didn''t know where to start, this is the book you''ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning, 2nd Edition teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math - the book will cover the rest. After an introduction to Python, you''ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models'' performance. You''ll also learn: How to use classic machine learning models like k-Nearest Neighbours, Random Forests, and Support Vector Machines, How neural networks work and how they''re trained, How to use convolutional neural networks, How to develop a successful deep learning model from scratch. You''ll conduct experiments along the way, building to a final case study that incorporates everything you''ve learned. This second edition is thoroughly revised and updated, and adds six new chapters to further your exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine tuning, transfer learning, object detection, semantic segmentation, multilabel classification, self-supervised learning, generative adversarial networks, and large language models. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning, 2nd Edition will give you the skills and confidence to dive into your own machine learning projects.

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

Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You''ll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. • 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 maximum long-term return.

Objev podobné jako Grokking Deep Reinforcement Learning - Miguel Morales

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

Objev podobné jako Learning Deep Learning - Magnus Ekman

Humans learn best from 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 MXNet that powers a suite of AWS machine learning products. Brandon Brown is a Machine Learning and Data Analysis blogger at outlace.com committed to providing clear teaching on difficult topics for newcomers.

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

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

Objev podobné jako Grokking Deep Learning - Andrew W Trask

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 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

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

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

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

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

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 projects. He’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

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

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

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

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

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

Objev podobné jako Generative Deep Learning - David Foster

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

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

In this Read with Oxford Stage 5: Biff, Chip and Kipper collection, Biff finds an exciting fossil on the beach, the children go back in time to the Middle Ages and put on a play in with a troupe of travelling actors and Nadim and Anneena help invent a flying machine!

Objev podobné jako Read with Oxford: Stage 5: Biff, Chip and Kipper: The Flying Machine and Other Stories - Roderick Hunt

This practical resource introduces readers to the design of field programmable gate array systems (FPGAs). Techniques and principles that can be applied by the engineer to understand challenges before starting a project are presented. The book provides a framework from which to work and approach development of embedded systems that will give readers a better understanding of the issues at hand and can develop solution which presents lower technical and programmatic risk and a faster time to market. Programmatic and system considerations are introduced, providing an overview of the engineering life cycle when developing an electronic solution from concept to completion. Hardware design architecture is discussed to help develop an architecture to meet the requirements placed upon it, and the trade-offs required to achieve the budget. The FPGA development lifecycle and the inputs and outputs from each stage, including design, test benches, synthesis, mapping, place and route and power estimation, are also presented. Finally, the importance of reliability, why it needs to be considered, the current standards that exist, and the impact of not considering this is explained. Written by experts in the field, this is the first book by "engineers in the trenches" that presents FPGA design on a practical level.

Objev podobné jako A Hands-On Guide to Designing Embedded Systems - Adam Taylor, Saket Srivastava, Dan Binnun

This practical guide presents a collection of repeatable, generic patterns to help guide the systems you build using common patterns and practices drawn from some of the highest performing distributed systems in use today.

Objev podobné jako Designing Distributed Systems - Brendan Burns

Skin79 Pore Designing, 100 ml, Krémové masky pro ženy, Máte problematickou, mastící se pleť s viditelnými nedokonalostmi a chcete rychle zlepšit její vzhled? Minerální čisticí jílová maska Skin79 Pore Designing se o to postará. Odstraní z pleti veškeré nečistoty, pomůže snížit tvorbu kožního mazu a pohltí nadměrnou mastnotu. Především však stáhne rozšířené póry, a zminimalizuje tak jejich viditelnost. Pleť bude po aplikaci dokonale čistá, ale i vyživená, hladká a rozzářená. Vlastnosti: důkladně a hloubkově čistí pleť, minimalizuje viditelnost rozšířených pórů snižuje produkci kožního mazu, pohlcuje mastnotu zklidňuje, působí proti podráždění, předchází znečištění má regenerační účinky, zmírňuje začervenání vyživuje, redukuje jemné vrásky udržuje pleť rozzářenou a hladkou Složení: bílý jíl, kyselina uhličitá – hloubkově čistí pleť, pomáhají minimalizovat viditelnost pórů extrakt z bílé vrby – snižuje tvorbu podráždění, stahuje cévy, zmírňuje začervenání extrakty z ibišku a avokáda – vyživují pleť a přispívají k odstranění drobných vrásek Jak aplikovat: Minerální čisticí jílová maska pro stažení pórů Skin79 Pore Designing se nanáší na předem vyčištěnou a tonizovanou pleť obličeje. Naberte přiměřené množství a rovnoměrně rozprostřete po obličeji. Po 10 minutách působení promasírujte tzv. T-zónu a poté opláchněte vlažnou vodou.

Objev podobné jako Skin79 Pore Designing minerální čisticí jílová maska pro stažení pórů 100 ml

SYSTEMS ENGINEERING HANDBOOK A comprehensive reference on the discipline and practice of systems engineering Systems engineering practitioners provide a wide range of vital functions, conceiving, developing, and supporting complex engineered systems with many interacting elements. The International Council on Systems Engineering (INCOSE) Systems Engineering Handbook describes the state-of-the-good-practice of systems engineering. The result is a comprehensive guide to systems engineering activities across any number of possible projects. From automotive to defense to healthcare to infrastructure, systems engineering practitioners are at the heart of any project built on complex systems. INCOSE Systems Engineering Handbook readers will find: Elaboration on the key systems life cycle processes described in ISO/IEC/IEEE 15288:2023;Chapters covering key systems engineering concepts, system life cycle processes and methods, tailoring and application considerations, systems engineering in practice, and more; andAppendices, including an N2 diagram of the systems engineering processes and a detailed topical index. The INCOSE Systems Engineering Handbook is a vital reference for systems engineering practitioners and engineers in other disciplines looking to perform or understand the discipline of systems engineering.

Objev podobné jako INCOSE Systems Engineering Handbook - INCOSE

Revised and updated sixth edition of the best-selling guide to branding fundamentals, strategy, and process. It’s harder than ever to be the brand of choice—in many markets, technology has lowered barriers to entry, increasing competition. Everything is digital and the need for fresh content is relentless. Decisions that used to be straightforward are now complicated by rapid advances in technology, the pandemic, political polarization, and numerous social and cultural changes. The sixth edition of Designing Brand Identity has been updated throughout to address the challenges faced by branding professionals today. This best-selling book demystifies branding, explains the fundamentals, and gives practitioners a roadmap to create sustainable and successful brands. With each topic covered in a single spread, the book celebrates great design and strategy while adding new thinking, new case studies, and future-facing, global perspectives. Organized into three sections—brand fundamentals, process basics, and case studies—this revised edition includes: Over 100 branding subjects, checklists, tools, and diagramsMore than 50 all-new case studies that describe goals, process, strategy, solutions, and resultsNew content on artificial intelligence, virtual reality, social justice, and evidence-based marketingAdditional examples of the best/most important branding and design work of the past few yearsOver 700 illustrations of brand touchpointsMore than 400 quotes from branding experts, CEOs, and design gurusWhether you’re the project manager for your company’s rebrand or you need to educate your staff or students about brand fundamentals, Designing Brand Identity is the quintessential resource. From research to brand strategy, design execution to launch and governance, Designing Brand identity is a compendium of tools for branding success and best practices for inspiration.

Objev podobné jako Designing Brand Identity - Wheeler Alina, Rob Meyerson

Joe Alves: Designing JAWS provides the production designer''s view into the development of this world-renowned film. Included are Joe''s stunning pre-production illustrations; handwritten location and production notes; on-set photographs; blueprints of the shark''s design; and first-time publication of his complete catalog of storyboards. Joe Alves: Designing JAWS is a must-have addition to every film reference library. Universal Studios'' JAWS is one of the most compelling and enduring movies ever made. Thrilling generations of audiences worldwide with its tight plot, memorable characters, and ground-breaking special effects - those that brought the great white shark to terrifying life even after many said it couldn''t be done. Buoyed by an energetic young director, Steven Spielberg, and through collaboration with trusted and equally determined production designer Joe Alves, the two proved integral to the making of this classic motion picture. Painstakingly compiled and written by Joe Alves'' biographer and JAWS expert, Dennis Prince, Joe Alves: Designing JAWS is a must-have addition to every film reference library.

Objev podobné jako Joe Alves: Designing Jaws - Dennis L. Prince

Create your own epic tabletop adventures Tabletop role-playing games (also known as TTRPGs) are social games that often center on playing fictional characters in a story or adventure. Designing TTRPGs For Dummies is an introductory romp into the creative and magical world of the game design process. This fun book guides you through character creation sheets, rule setting, worldbuilding, and beyond. With this book at your side, you'll roll for initiative by learning what roleplaying games are, the history of TTRPGs, how to take an idea and turn it into a playable game, and how to perfect gameplay experiences. A great TTRPG is full of creativity, surprise, and storytelling. This easy-to-follow guide teaches you the secrets to creating immersive worlds of your own. Inside Explore the exciting world of tabletop roleplaying games and create your ownFollow step-by-step instructions on fleshing out your game idea, testing it out with players, and improving the overall game experienceGet tips for designing a compelling game world, creating rules that keep games moving, and handling unexpected player choicesFind out how and where to publish your completed TTRPG Designing TTRPGs For Dummies is your magical guide to creating fun adventures you can play with friends and even share with the world.

Objev podobné jako Designing TTRPGs For Dummies - Martin Buinicki

Sušenky pro děti vhodné pro děti od 12 měsíců, bez konzervantů a umělých barviv, bez lepku, bez přidaného cukru, bio kvalita a bez palmového oleje Svým dětem se přirozeně snažíme dávat to nejlepší a nejkvalitnější. Proto jsou bio ovocné chipsy Freche Freunde z lesních plodů ideální volbou. Ovocné chipsy Freche Freunde jsou lyofilizované, tedy sušené mrazem, a představují skvělou svačinku pro děti, ať už jdou na výlet, do školky, do školy nebo si potřebují doma vzít něco rychlého. Zahoďte nezdravé brambůrky a dejte dětem zdravé mlsání. Chipsy jsou bez přidaného cukru, vhodné i pro vegany, bezlepkové a složení je 100% přírodní bez jakékoli chemie. Hodí se také, pokud vaše děti bojují s obezitou, protože jsou vyrobeny pouze z ovoce. Ovocné chipsy Freche Freunde jsou v balení 3 × 10 g. Klíčové vlastnosti bio ovocných chipsů Freche Freunde - Lesní plody, 3 × 10 gBio ovocné chipsy Freche Freunde pro vaše nejmenšíS příchutí lesních plodů, lyofilizované

Objev podobné jako Freche Freunde BIO Ovocné chipsy - Lesní plody mix 3× 10 g (0745110150008)

Sušenky pro děti vhodné pro děti od 12 měsíců, bez konzervantů a umělých barviv, bez lepku, bez přidaného cukru, bez přídavku soli, bio kvalita a bez palmového oleje Svým dětem se přirozeně snažíme dávat to nejlepší a nejkvalitnější. Proto jsou bio jahodové chipsy Freche Freunde ideální volbou. Ovocné chipsy Freche Freunde jsou lyofilizované, tedy sušené mrazem, a představují skvělou svačinku pro děti, ať už jdou na výlet, do školky, do školy nebo si potřebují doma vzít něco rychlého. Zahoďte nezdravé brambůrky a dejte dětem zdravé mlsání. Chipsy jsou bez přidaného cukru, vhodné i pro vegany, bezlepkové a složení je 100% přírodní bez jakékoli chemie. Hodí se také, pokud vaše děti bojují s obezitou, protože jsou vyrobeny pouze z ovoce. Klíčové vlastnosti bio ovocných chipsů Freche Freunde - Jahoda, 12 gBio ovocné chipsy Freche Freunde pro vaše nejmenšíS příchutí jahody, lyofilizovanéSkvělá svačinka do školy, školky nebo na výletyBez přidaného...

Objev podobné jako Freche Freunde BIO Ovocné chipsy - Jahoda 12 g (4260249140530)

Sušenky pro děti vhodné pro děti od 12 měsíců, bez konzervantů a umělých barviv, bez lepku, bez přidaného cukru, bez přídavku soli, bio kvalita a bez palmového oleje Svým dětem se přirozeně snažíme dávat to nejlepší a nejkvalitnější. Proto jsou bio ovocné chipsy Freche Freunde z lesních plodů ideální volbou. Ovocné chipsy Freche Freunde jsou lyofilizované, tedy sušené mrazem, a představují skvělou svačinku pro děti, ať už jdou na výlet, do školky, do školy nebo si potřebují doma vzít něco rychlého. Zahoďte nezdravé brambůrky a dejte dětem zdravé mlsání. Chipsy jsou bez přidaného cukru, vhodné i pro vegany, bezlepkové a složení je 100% přírodní bez jakékoli chemie. Hodí se také, pokud vaše děti bojují s obezitou, protože jsou vyrobeny pouze z ovoce. Klíčové vlastnosti bio ovocných chipsů Freche Freunde - Lesní plody, 10 gBio ovocné chipsy Freche Freunde pro vaše nejmenšíS příchutí lesních plodů, lyofilizovanéSkvělá svačinka do školy, školky...

Objev podobné jako Freche Freunde BIO Ovocné chipsy - Lesní plody mix 10 g (4260618523186)

Zdravé chipsy - výrobcem uváděná energetická hodnota 384 kcal, hmotnost 80 g Změna barevného stylu šablony pro sportovní výživu Hrachové chipsy s cvrččím proteinem SENS 80 g Hrachové chipsy SENS představují skvělou alternativu ke klasickým brambůrkům. Obsahují hrachovou i cvrččí mouku, díky čemuž jsou vhodné nejen pro sportovce. Hrachové chipsy SENS ukrývají ve svých 100 g 24,6 g bílkovin a 19,6 g vlákniny. Báječnou chuť chipsům dodává mák i směs koření. Naproti tomu jsou zcela bez lepku a palmového oleje. Klíčové vlastnosti hrachových chipsů s cvrččím proteinem SENS Hrachové chipsyObsahují hrachovou i cvrččí moukuVhodné nejen pro sportovce...

Objev podobné jako SENS Protein chipsy s cvrččím proteinem 80g, mák a mořská sůl (4260624010700)

DVD film Mach a Šebestová na prázdninách [1999]. Český večerníček. Dolby Digital. Celovečerní film vychází ze seriálu a je doplněn novými příhodami této populární dvojice MACHA A ŠEBESTOVÉ. Celovečerní film vychází ze seriálu a je doplněn novými příhodami této populární dvojice MACHA A ŠEBESTOVÉ. DVD obsahuje epizody: 1-Jak Mach a Šebestová jeli na prázdniny•2-Jak Jonatán chytil blechu•3-Jak Mach a Šebestová udělali z dědečka tarzana•4-Jak Mach a Šebestová prožili deštivé odpoledne•5-Jak Mach a Šebestová udělali z malíře Kolouška žáka Leonarda da Vinci•6-Jak Mach a Šebestová poslali lístek paní Kadrnožkové•7-Jak Mach a Šebestová potrestali paní Tláskalovou•8-Jak Mach a Šebestová zavinili zmizení Lukáše Tůmy•9-Jak Mach a Šebestová splnili životní sen paní Janderkové•10-Jak Šebestovi přijeli za dcerou na víkend•11-Jak Mach a Šebestová navštívili cirkus•12-Jak se stal Jonatán hrdinou dne•13-Jak se Mach a Šebestová vrátili z prázdnin

Objev podobné jako Mach a Šebestová na prázdninách (DVD)

DVD film Mach a Šebestová na prázdninách [1999]. Český večerníček. Dolby Digital. Papírový obal. Celovečerní film vychází ze seriálu a je doplněn novými příhodami této populární dvojice MACHA A ŠEBESTOVÉ. Celovečerní film vychází ze seriálu a je doplněn novými příhodami této populární dvojice MACHA A ŠEBESTOVÉ. DVD obsahuje epizody: 1-Jak Mach a Šebestová jeli na prázdniny•2-Jak Jonatán chytil blechu•3-Jak Mach a Šebestová udělali z dědečka tarzana•4-Jak Mach a Šebestová prožili deštivé odpoledne•5-Jak Mach a Šebestová udělali z malíře Kolouška žáka Leonarda da Vinci•6-Jak Mach a Šebestová poslali lístek paní Kadrnožkové•7-Jak Mach a Šebestová potrestali paní Tláskalovou•8-Jak Mach a Šebestová zavinili zmizení Lukáše Tůmy•9-Jak Mach a Šebestová splnili životní sen paní Janderkové•10-Jak Šebestovi přijeli za dcerou na víkend•11-Jak Mach a Šebestová navštívili cirkus•12-Jak se stal Jonatán hrdinou dne•13-Jak se Mach a Šebestová vrátili z prázdnin

Objev podobné jako Mach a Šebestová na prázdninách (DVD) (papírový obal)

***Winner of the Financial Times Business Book of the Year award*** ***Selected as one of Barack Obama's Favourite Books of 2023*** 'Pulse quickening. A nonfiction thriller - equal parts The China Syndrome and Mission Impossible' New York Times An epic account of the decades-long battle to control the world's most critical resource—microchip technology Power in the modern world - military, economic, geopolitical - is built on a foundation of computer chips. America has maintained its lead as a superpower because it has dominated advances in computer chips and all the technology that chips have enabled. (Virtually everything runs on chips: cars, phones, the stock market, even the electric grid.) Now that edge is in danger of slipping, undermined by the naïve assumption that globalising the chip industry and letting players in Taiwan, Korea and Europe take over manufacturing serves America's interests. Currently, as Chip War reveals, China, which spends more on chips than any other product, is pouring billions into a chip-building Manhattan Project to catch up to the US. In Chip War economic historian Chris Miller recounts the fascinating sequence of events that led to the United States perfecting chip design, and how faster chips helped defeat the Soviet Union (by rendering the Russians’ arsenal of precision-guided weapons obsolete). The battle to control this industry will shape our future. China spends more money importing chips than buying oil, and they are China's greatest external vulnerability as they are fundamentally reliant on foreign chips. But with 37 per cent of the global supply of chips being made in Taiwan, within easy range of Chinese missiles, the West's fear is that a solution may be close at hand. 'A riveting history. Features vivid accounts and colourful characters' Financial Times'Fascinating…A historian by training, Miller walks the reader through decades of semiconductor history – a subject that comes to life thanks to [his] use of colorful anecdotes' Forbes 'Indispensable' Niall Ferguson

Objev podobné jako Chip War - Chris Miller

Sustainable Hybrid Energy Systems Discovering comprehensive approaches to build sustainable hybrid energy systems Hybridization is the eternal theme of human energy utilization. However, it has never been more important than it is now because of the urgency of promoting energy transition and achieving carbon neutrality. Therefore, exploring the design, combustion, operation, and policy challenges of sustainable hybrid energy systems becomes increasingly important. Sustainable Hybrid Energy Systems: Carbon Neutral Approaches, Modeling, and Case Studies provides a detailed explanation of these aspects. Dividing hybrid energy systems into three categories—co-located, co-combusted, and co-operated, this book emphasizes the deployment optimization, emission quota allocation, scheduling coordination, and renewable portfolio standards implementation of these systems. The results are essential tools for understanding the current and future of multi-input single-output hybrid energy systems. Sustainable Hybrid Energy Systems readers will also find: Clear logical framework that reveals the constitutes of hybrid energy systems. Systematic technical scheme for building an economic, environmental, flexible, and resilient future energy system. Extensive case studies from single power plant level, multiple power plant level, and grid level. Effective guidelines for wider application of the proposed carbon neutral approaches. Sustainable Hybrid Energy Systems is ideal for power engineers, electrical engineers, scientists in industry, and environmental researchers looking to understand these energy solutions. It will also provide collectible value for libraries.

Objev podobné jako Sustainable Hybrid Energy Systems - Fengjuan Wang, Jiuping Xu

Systems Ultra explores how we experience complex systems: the mesh of things, people, and ideas interacting to produce their own patterns and behaviours.What does it mean when a car which runs on code drives dangerously? What does massmarket graphics software tell us about the workplace politics of architects? And, in these human-made systems, which phenomena are designed, and which are emergent? In a world of networked technologies, global supply chains, and supranational regulations, there are growing calls for a new kind of literacy around systems and their ramifications. At the same time, we are often told these systems are impossible to fully comprehend and are far beyond our control.Drawing on field research and artistic practice around the industrial settings of ports, air traffic control, architectural software, payment platforms in adult entertainment, and car crash testing, Georgina Voss argues that complex systems can be approached as sites of revelation around scale, time, materiality, deviance, and breakages. With humour and guile, she tells the story of what ‘systems’ have come to mean, how they have been sold to us, and the real-world consequences of the power that flows through them.Systems Ultra goes beyond narratives of technological exceptionalism to explore how we experience the complex systems which influence our lives, how to understand them more clearly, and, perhaps, how to change them.

Objev podobné jako Systems Ultra - Georgina Voss