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

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

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

Objev podobné jako Grokking Machine Learning - Luis Serrano

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

Objev podobné jako Machine Learning - Jason Bell

The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you ll find machine learningâ ”the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you ll dive into the tools, code, and math that make machine learning goâ ”and you ll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to. Piece together what machine learning is, what it can do, and what it can t doLearn the basics of machine learning code and how it integrates with large datasetsUnderstand the mathematical principles that AI uses to make itself smarterConsider real-world applications of machine learning and write your own algorithms With clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.

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

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning â ”also known as data mining or data analyticsâ ” is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using RAn expanded chapter focused on discussion of deep learning techniquesA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniquesEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presentedA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

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

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

Objev podobné jako Machine Learning Algorithms in Depth - Vadim Smolyakov

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

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

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

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

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

Objev podobné jako Probabilistic Machine Learning - Kevin P. Murphy

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

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

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 impactful guide takes you through the process of effective reflective practice with a clear and creative focus on helping you be successful in your professional and academic life. It will provide you with clear guidance about how to undertake the reflective writing needed to achieve academic qualifications and professional accreditations.This book includes:·    Practical strategies undertaking reflective practice in service of your clients·    Recommendations for writing up your reflective practice for of assessment and accreditation purposes·    Case studies and templates that can be applied to a wide range of professional fields and academic contexts·    A theoretical foundation underpinning the purpose and intention of professional reflective practice·    Practical advice from experienced reflective practitioners to help you put the principles into practice.This engaging and thought-provoking book will give you confidence with thinking critically and reflectively about your work so that you can achieve better outcomes for yourself and others.

Objev podobné jako Your Essential Guide to Effective Reflective Practice - Christian van Nieuwerburgh, David Love

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

Komplexní encyklopedická kniha o tancích zachycuje celou historii těchto obrněných vozidel a v závěru nabízí i výhled do budoucna. Více než 400 typů tanků, včetně bezpilotních. Dozvíte se o amerických, britských, ruských či francouzských strojích, o slavných konstruktérech, jakými byli Michail Koškin nebo sir William Tritton. Kniha je doprovázena detailními velkolepými fotografiemi.

Objev podobné jako Tanky - David Willey

Even if youâ ™re used to managing small teams, youâ ™ll find the dynamics of a big team very different. As projects scale up in size and complexity, the skills you need to manage effectively change. Itâ ™s less about the technical aspects of project delivery, more about integrating different sub teams into a cohesive whole. Luckily, team specialist and major projects advisor Tony Llewellyn is here to provide the practical guidance you need to set up and run an effective team of teams â “ in just 6 minutes!

Objev podobné jako Managing Big Teams - Tony Llewellyn

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

Objev podobné jako Deep Learning with Python - François Chollet

If you ve been curious about artificial intelligence and machine learning but didn t know where to start, this is the book you ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning, 2nd Edition teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math - the book will cover the rest. After an introduction to Python, you ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models performance. You ll also learn: How to use classic machine learning models like k-Nearest Neighbours, Random Forests, and Support Vector Machines, How neural networks work and how they re trained, How to use convolutional neural networks, How to develop a successful deep learning model from scratch. You ll conduct experiments along the way, building to a final case study that incorporates everything you ve learned. This second edition is thoroughly revised and updated, and adds six new chapters to further your exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine tuning, transfer learning, object detection, semantic segmentation, multilabel classification, self-supervised learning, generative adversarial networks, and large language models. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning, 2nd Edition will give you the skills and confidence to dive into your own machine learning projects.

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

Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You ll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. â ¢ Foundational reinforcement learning concepts and methods â ¢ The most popular deep reinforcement learning agents solving high-dimensional environments â ¢ Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return.

Objev podobné jako Grokking Deep Reinforcement Learning - Miguel Morales

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

NVIDIA s Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals. -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA Ekman uses a learning technique that in our experience has proven pivotal to successâ ”asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us. -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today s exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience.After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images.Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA s invention of the GPU sparked the PC gaming market. The company s pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others.Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Objev podobné jako Learning Deep Learning - Magnus Ekman

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

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

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

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 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

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

David is ready for class in this hilarious schooltime companion to the Caldecott Honor-winning No, David! by bestselling picture book creator David Shannon!David s teacher has her hands full. From running in the halls to chewing gum in class, David s high-energy antics fill each school day with trouble -- and are sure to bring a smile to even the best-behaved reader.David Shannon continues to entertain us with young David s mischievous antics and a lighthearted story that s sure to leave readers of all ages laughing. With millions of copies in print and five sequels, No, David! hit the ground running in 1998 and was a Caldecott Honor Book, a New York Times Best Illustrated Book, and a classic for over 25 years.

Objev podobné jako David Goes to School

CD album Michal David - Michal David Funky (2022). 22 nejznámějších písniček Michala Davida v nové funky verzi. Největší hity Michala Davida ve funky provedení. 20 největších a nejposlouchanějších hitů, které Michal David upravil do stylu funky. Na CD se nachází i dva bonusy s letním hitem ,,Kde si včera bol,, nahráno v tandemu s RomStar Vyškov a Načo pojdem domov s MilkoBrothers. 1. Právě začínáme 03:17 2. my máme prima rodiče 02:45 3. Po cestách růžových 03:21 4. Největší z nálezů a ztrát 03:48 5. Žádná bouřka věčně netrvá 03:40 6. Discopříběh 04:28 7. To zas byl den 03:07 8. Colu, pijeme colu 03:27 9. Vidím tě všude 03:27 10. Trhnout se 03:26 11. Céčka, sbírá céčka 03:16 12. Decibely lásky 04:05 13. Pláč 03:51 14. Když jsou doma galeje 03:19 15. Bláznivá noc (Sbor diváci Brno) 03:53 16. Děti ráje 03:56 17. Nonstop 03:22 18. Pár přátel 4.06 19. Správnej čas 03:35 20. Celebration 03:38 21. Bonus – Kde si včera bol 03:17 22. Bonus – Načo pojdem domov 03:02

Objev podobné jako Michal David - Michal David Funky (CD)

Lovci a sběrači byli organizovanější, než si myslíte. A některá z prvních měst svobodnější, než by vás napadlo.Dlouhé tisíce let jsme údajně žili v absolutně rovnostářských kmenech – než přišlo zemědělství, bohatství a chudoba, byrokracie a tyrani. Tento jednoduchý příběh milují učitelé dějepisu i autoři bestsellerů o historii. Profesoři David Graeber a David Wengrow však na množství příkladů dokládají, že jde o pouhý výmysl. Skutečnost byla nesrovnatelně rozmanitější a zajímavější, než nás učili ve školních lavicích.Kniha Úsvit všeho stojí na přesvědčivých, dosud často opomíjených faktech z archeologie, historie a kulturní antropologie. Stala se světovou událostí a vyvolala diskuse, které budou probíhat ještě dlouhá léta.Autoři oživují fascinující svět, v němž naši dávní předci nebyli primitivové žijící v tlupách, ale aktivní členové pozoruhodných společností, první města zakládali lovci a sběrači, mimo jiné na území dnešní Ukrajiny, zemědělství nebylo revolucí – rozvíjelo se tisíce let a některé společnosti se od něj znovu odvracely, archeologie objevila obrovská města bez byrokracie, šlechty a tyranů, lidé odpradávna experimentovali s různými podobami demokracie a pružně přizpůsobovali své rozhodovací systémy. Graeber a Wengrow nevolají po návratu na stromy. Na základě průkopnických objevů nám připomínají, že civilizaci lze budovat i bez ztráty svobody.O autorechDavid Graeber (1961–2020) byl americký antropolog a anarchistický aktivista, profesor na Yaleově univerzitě, University of London a London School of Economics. Napsal knihy Dluh: prvních 5000 let nebo Utopie pravidel. Úsvit všeho je jeho nejrozsáhlejším a posledním dílem, na němž s Davidem Wengrowem pracoval deset let. Zemřel náhle, tři týdny po jeho dokončení.David Wengrow (*1972) je britský archeolog, profesor komparativní archeologie na University College London. Vedl terénní výzkumy v Africe a na Blízkém východě. Publikuje také v The Guardian a The New York Times.Ohlasy„Fascinující, radikální a hravý úvod do zdánlivě vyčerpaného žánru velké evoluční historie lidstva. Nesnaží se o nic menšího než o úplné převrácení základů, na kterých spočívá běžný narativ... Erudované, přesvědčivé a často pozoruhodně zábavné.“ — Boston Review„Graeber a Wengrow nabízejí historii posledních 30 000 let, která je nejen divoce odlišná od všeho, na co jsme zvyklí, ale také mnohem zajímavější: strukturovaná, překvapivá, paradoxní, inspirující... Jejich cílem je nahradit dominantní velké vyprávění nikoli vlastní smyšlenkou, ale obrysem obrazu lidské minulosti plné politických experimentů a kreativity.“ — The Atlantic„Skvělá kniha, o které se bude ještě hodně debatovat. Změní pohled na historii a nasměruje výzkum k úplně novým obzorům.“ — Science

Objev podobné jako Úsvit všeho - David Graeber, David Wengrow

A collection of legendary British artist David Hockney’s insights into art, life, nature, creativity and much more. ‘I’ve always been a looker ... that’s what artists do’ This anthology of quotations by David Hockney follows in the successful format of ‘The World According to’ series.Ranging across topics including drawing, photography, nature, creativity, the internet and much more, The World According to David Hockney offers a delightful and engaging overview of the artist’s inimitable spirit, personality and opinions. From everyday observations – ‘The eye is always moving; if it isn’t moving you are dead’ – to artistic insights such as ‘painted colour always will be better than printed colour, because it is the pigment itself’, as well as musings on other image makers, including Caravaggio, Cézanne and Hokusai, Hockney has a knack for capturing profound truths in pithy statements. Born in Bradford, England, in 1937, Hockney attended art school in London before moving to Los Angeles in the 1960s.There, he painted his famous swimming pool paintings, and since then has embraced a range of media including photocollage, video and digital technologies. In a 2011 poll of more than 1,000 British artists, Hockney was voted the most influential British artist of all time. Presented as a beautifully designed and attractive package, illustrated with works of art from throughout Hockney s career, this is the perfect gift for art lovers everywhere.

Objev podobné jako The World According to David Hockney - David Hockney, Martin Gayford

INSTANT NEW YORK TIMES, USA TODAY, WALL STREET JOURNAL, AND INTERNATIONAL BESTSELLER Discover #1 New York Times bestselling author David Bach’s three secrets to financial freedom in an engaging story that will show you that you are richer than you think. Drawing on the author’s experiences teaching millions of people around the world to live a rich life, this fast, easy listen reveals how anyone—from millennials to baby boomers—can still make his or her dreams come true.In this compelling, heartwarming parable, Bach and his bestselling coauthor John David Mann (The Go-Giver) tell the story of Zoey, a twenty-something woman living and working in New York City. Like many young professionals, Zoey is struggling to make ends meet under a growing burden of credit card and student loan debt, working crazy hours at her dream job but still not earning enough to provide a comfortable financial cushion. At her boss’s suggestion, she makes friends with Henry, the elderly barista at her favorite Brooklyn coffee shop. Henry soon reveals his “Three Secrets to Financial Freedom,” ideas Zoey dismisses at first but whose true power she ultimately comes to appreciate. Over the course of a single week, Zoey discovers that she already earns enough to secure her financial future and realize her truest dreams—all she has to do is make a few easy shifts in her everyday routine. The Latte Factor demystifies the secrets to achieving financial freedom, inspiring you to realize that it’s never too late to reach for your dreams. By following the simple, proven path that Henry shows Zoey, anyone can make small changes today that will have big impact for a lifetime, proving once again that “David Bach is the financial expert to listen to when you’re intimidated by your finances” (Tony Robbins, #1 New York Times bestselling author of Money: Master the Game).

Objev podobné jako The Latte Factor - John David Mann, David Bach

LP vinyl - Vinylová reedice pátého alba původně vydaného v roce 2011. Slavný producent na albu redefinoval pojem EDM a nabídl hity jako Where Them Girls At, Turn Me On nebo Titanium. Vinylová reedice pátého alba původně vydaného v roce 2011. Slavný producent na albu redefinoval pojem EDM a nabídl hity jako Where Them Girls At, Turn Me On nebo Titanium. David Guetta je králem tanečních parketů a jeden z nejpopulárnějších producentů taneční hudby, která je často označovaná zkratkou EDM. Je držitelem mnoha rekordů a ocenění včetně Grammy. Již několik let patří i u nás mezi nejžádanější interprety mezi posluchači hitových rádiích. Seznam stop LP1 Where Them Girls At (feat. Nicki Minaj amp; Flo Rida) / Little Bad Girl (feat. Taio Cruz amp; Ludacris) / Turn Me On (feat. Nicki Minaj) / Sweat (Snoop Dogg vs. David Guetta) [David Guetta Remix] / Without You (feat. Usher) / Nothing Really Matters (feat. will.i.am) / I Can Only Imagine (feat. Chris Brown amp; Lil...

Objev podobné jako Guetta David: Nothing But The Beat (2x LP) - LP (0838951)

Úplne nový a revolučný pohľad na evolúciu ľudstva, ktorý našim predkom vracia dlho popieranú ľudskosť. Na základe najnovších výskumov antropológ David Graeber a archeológ David Wengrow radikálne prehodnotili dejiny človeka od samých počiatkov v časoch lovcov a zberačov, cez vznik poľnohospodárstva, prvých miest a štátov. Dokazujú, že naši predkovia boli inteligentní ľudia, ktorí sa nehnali len za potravou ani sa bezhlavo navzájom nezabíjali.Ako teda trávili čas? Akí boli, čo robili, ako fungovali dávnoveké spoločnosti, dodnes považované za primitívne a jednoduché, hoci najnovšie poznatky ukazujú, že to tak nebolo? Počiatok všetkého je dôkladná štúdia dvoch naslovovzatých odborníkov a nadšencov, ktorí našli odpovede na všetky tieto otázky.

Objev podobné jako Počiatok všetkého - David Graeber, David Wengrow

Toto je příběh Davida Placzka, nejstaršího syna židovských idealistických průkopníků v Palestině - německé matky a českého otce. David zde bohatým a jedinečným jazykem popisuje, jaký byl jeho každodenní život a jak přežil holokaust daleko od své vlasti a domova. Svou osobní historii prokládá dějinami světa, vzpomínkami, které obsahují jak dojmy z první ruky, tak historická fakta. Je to svědectví o dobru i zlu, o schopnosti lidstva tvořit, nebo děsivě ničit. Je to příběh, který ukazuje, jak i jednoduchá rozhodnutí, ať už ukvapená či racionální, nemilosrdně určují další běh lidského života. David dokončil psaní svých pamětí v roce 2004 v australském Melbourne. Před svou smrtí v září 2008 nás požádal, aby byl jeho popel rozptýlen v zátoce Port Phillip Bay, kudy do Austrálie vstoupil. Chtěl tím vyjádřit svůj vděk zemi, která se mu stala domovem. Zanechal po sobě pouze soubor se svým příběhem, kterému jsem se souhlasem rodiny jako Davidova milovaná neteř směla dát v roce 2016 knižní podobu. Česky nyní kniha vychází poprvé. S láskyplnou vzpomínkou ji věnujeme autorovi - Davidu Placzkovi.

Objev podobné jako Paměti. David Placzek - David Placzek

THE NEW YORK TIMES BESTSELLER AND SUNDAY TIMES, OBSERVER AND BBC HISTORY BOOK OF THE YEARFINALIST FOR THE ORWELL PRIZE FOR POLITICAL WRITING 2022 Pacey and potentially revolutionary Sunday Times Iconoclastic and irreverent ... an exhilarating read The Guardian For generations, our remote ancestors have been cast as primitive and childlike - either free and equal, or thuggish and warlike. Civilization, we are told, could be achieved only by sacrificing those original freedoms or, alternatively, by taming our baser instincts. David Graeber and David Wengrow show how such theories first emerged in the eighteenth century as a reaction to indigenous critiques of European society, and why they are wrong. In doing so, they overturn our view of human history, including the origins of farming, property, cities, democracy, slavery and civilization itself. Drawing on path-breaking research in archaeology and anthropology, the authors show how history becomes a far more interesting place once we begin to see what s really there. If humans did not spend 95 per cent of their evolutionary past in tiny bands of hunter-gatherers, what were they doing all that time? If agriculture, and cities, did not mean a plunge into hierarchy and domination, then what kinds of social and economic organization did they lead to? The answers are often unexpected, and suggest that the course of history may be less set in stone, and more full of playful possibilities than we tend to assume. The Dawn of Everything fundamentally transforms our understanding of the human past and offers a path toward imagining new forms of freedom, new ways of organizing society. This is a monumental book of formidable intellectual range, animated by curiosity, moral vision and faith in the power of direct action. This is not a book. This is an intellectual feast Nassim Nicholas Taleb The most profound and exciting book I ve read in thirty years Robin D. G. Kelley

Objev podobné jako The Dawn of Everything - David Graeber, David Wengrow

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

Joe the Bouncer seeks a killer on the darkest sides of New York City in this captivating hard-boiled mystery from David Gordon. Joe Brody, ex-Special Forces operative with post-traumatic stress syndrome so severe it drove him to drug abuse and almost ruined him, is getting his life back together. Living with his grandmother in Queens, Joe has taken a simple job as a strip club bouncer, a role where he can spend his nights reading the classics. The only catch is that his childhood friend Gio Caprisi, now head of New York s Italian Mafia, relies on Joe s extra-legal expertise when things get particularly nasty on the streets.Recently, New York s criminal underworld has been shaken by the disappearance of its most successful call girls. As a pattern emerges, the women s sudden departures come to resemble something deeply concerning â “ the work of a serial kidnapper. When a woman turns up dead, the hunt for the predator behind it all becomes even more urgent. To find the killer, Joe will have to plunge into the seediest fringes of Manhattan and its surrounding boroughs on another wild ride.Reviews for David Gordon David Gordon brings an outstanding new voice to the contemporary crime novel Robert Crais A unique and worthwhile series CrimeReads Gordon knows how to write a potboiler LA Times

Objev podobné jako The Wild Life - David Gordon

Part photo-book, part art-book, The Grand Illusion of David Bowie is a celebration of the life and prestigious talents of one of the most extraordinary individuals of the Twentieth Century. Eisner Award-nominated author David Currie takes us through Bowie s career, sharing his concert photography from the Eighties, while recounting stories of living during the times of the Starman and of meeting the man himself on many occasions. All Bowie fans will love this high-quality hardback book and it will look great on coffee tables around the globe. Artist George Underwood, Bowie s pal since childhood, also shares memories of his long-time friend, together with rare personal photographs and the majority of his iconic Bowie artwork is collected together in this book, which also features influences of science fiction and comic books on Bowie, plus stunning artwork from world-renowned artists that have been produced just for this book.

Objev podobné jako The Grand Illusion of David Bowie - David Currie

Joe the Bouncer s search for a stolen racing pigeon sends him into a warren of assassins in this thrilling caper from David Gordon. Harvard dropout and ex-Special Forces operative Joe Brody is climbing the ranks in the criminal underworld. After successfully executing multiple missions for the various crime syndicates that run New York City, he has come to earn the trust and respect of the cityâ ™s most dangerous denizens. Which is why his newest task â ” retrieving a pet pigeon snatched from a rooftop coop in Brooklyn â ” has Joe puzzled â ¦ until he learns that the bird is valued at close to a million dollars. Joe hatches a plan to sneak into the luxury apartment building where the pigeon is held captive. But the plan takes a deadly turn when he stumbles upon a nest of international war criminals. Fearing that Joe s entry into the building has somehow compromised their nefarious scheme, they put a bounty on his head. In New York, Joe is untouchable, but his new foes come from outside the flock, and heâ ™ll need a wing and a prayer to elude their assassins. Reviewers on David Gordon David Gordon brings an outstanding new voice to the contemporary crime novel. Robert Crais A unique and worthwhile series CrimeReads Gordon knows how to write a potboiler. LA Times In the caper tradition popularized by Donald E. Westlake and Lawrence Block, Gordon uses humour to good effect. Publishers Weekly

Objev podobné jako The Pigeon - David Gordon

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

Workshop Machining is a comprehensive textbook that explains the fundamental principles of manually operating machinery to form shapes in a variety of materials. It bridges the gap between people who have traditional toolmaking skills and those who have been trained in programming and operation of CNC machines in a focused production environment, rather than general machine shop. Using a subject-based approach, David Harrison intuitively guides readers and supplies practical skills. The chapters cover everything from the basic machine controls to advanced cutting operations using a wide range of tooling and work-holding devices. Theory and practice are shown via a mixture of diagrams, text and illustrated worked examples, as well as through exercises.The book is ideal for students and lecturing staff who participate in, or lead, practical machining sessions, and for those who wish to further develop their machining skills. It also serves as an excellent reference to understand the principles and limitations of producing shapes with cutters that move in a limited combination of linear and radial paths.

Objev podobné jako Workshop Machining - Harrison David

Learn key concepts of linear algebra to equip yourself in your studies and future career. Linear Algebra and Its Applications 6th edition by Steven R. Lay, Judi J. McDonald and David C. Lay is an excellent introductory guide to the principles and foundations of practical linear algebra. With its learner-friendly approach, the textbook starts with easier material, building confidence by introducing typically challenging concepts early on and gradually developing them. The book revisits those concepts throughout, ensuring you do not become overwhelmed when abstract concepts are introduced, as you progress with your learning. The latest edition provides new and revised content, with a range of features, including: A broad range of introductory vignettes, application examples, and online resources New material and topics to consolidate and enhance your understanding of the subject New, modernised applications to prepare your learning of the most innovative topics, such as machine learning, Artificial Intelligence, and digital signal processing With an array of exercises and questions to support your learning, this textbook provides the tools you need to build on your understanding of linear algebra and succeed in your studies. Also available with MyLab® Math MyLab is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab Math personalises the learning experience and improves results for each student. If you would like to purchase both the physical text and MyLab® Math, search for: 9781292351353 Linear Algebra and Its Applications, Global Edition, 6th edition plus MyLab Math with Pearson eText. Package consists of: 9781292351216 Corporate Finance, Global Edition, 5th Edition 9781292351285 Corporate Finance, Global Edition, 5th Edition MyLab® Math with Pearson eText MyLab® Math is not included. Students, if MyLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. MyLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information. This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content, which is especially relevant to students outside the United States.

Objev podobné jako Linear Algebra and Its Applications, Global Edition - David Lay, Steven Lay, Judi McDonald

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

Research Methods: A Practical Guide for Students and Researchers is a practical guide on how to conduct research systematically and professionally. The book begins by distinguishing between causal and interpretive sciences. It then guides the reader on how to formulate the research question, review the literature, develop the hypothesis or framework, select a suitable research methodology, and analyze both quantitative and qualitative data.The book uses classic examples as exemplars. It also uses many examples from different disciplines and sectors to demonstrate and showcase the inter-connections and wider applications of research tools.The book emphasizes integration. It does not merely provide a smorgasbord of research designs, data collection methods, and ways to analyze data. Instead, it shows how one could formulate research strategies given the outcomes the researchers are required or tasked to deliver.The revised edition includes three new chapters on time series (including spatial models), machine learning, and meta-analysis. In addition, existing chapters have been expanded to include more examples, digital research, and new material.

Objev podobné jako Research Methods: A Practical Guide For Students And Researchers - Willie Tan

Computer science can feel unapproachable for those without a formal CS education. Fun Computer Science Projects in Python pulls back that curtain, illuminating several foundational CS concepts through creative, hands-on projects. Each of the 7 projects is presented in a code-centric tutorial that gently introduces topics like interpreters, emulators, and machine learning without getting bogged down by complex theory. The projects showcase advanced Python language features and clean code principles while exploring interesting algorithms. Chapters conclude with discussions of real-world applications of the topic and proposed exercises to extend the reader s skills. Covers Python 3.x

Objev podobné jako Computer Science From Scratch - David Kopec

Based on the author⠙s many years of consulting experience with teams in the public and private sectors, Creating Effective Teams: A Guide for Members and Leaders describes why teams are important, how they function, and what makes them productive. Susan A. Wheelan covers in depth the four stages of a team⠔forming, storming, norming, and performing⠔clearly illustrating the developmental nature of teams and describing what happens in each stage. Separate chapters are devoted to the responsibilities of team leaders and team members. Problems that occur frequently in groups are highlighted, followed by what-you-can-do sections that offer specific advice. Real-life examples and questionnaires are used throughout the book, giving readers the opportunity for self-evaluation.

Objev podobné jako Creating Effective Teams - International Student Edition - Christian Jacobsson, Susan A. Wheelan, Maria Akerlund