Mathematics for Machine Learning

Mathematics for Machine Learning
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
Total Pages: 392
Release: 2020-04-23
Genre: Computers
ISBN: 1108569323

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.

Professional VB.NET

Professional VB.NET
Author: Fred Barwell
Publisher: Wrox
Total Pages: 670
Release: 2002-04-30
Genre: Computers
ISBN: 9780764544002

What is this book about? .NET is designed to provide a new environment within which you can develop almost any application to run on Windows (and possibly in the future on other platforms). Visual Basic .NET (VB.NET) is likely to be a very popular development tool for use with this framework. VB.NET is a .NET compliant language and, as such, has (except for legacy reasons) almost identical technical functionality as the new C# language and Managed Extensions for C++. Using VB.NET, you can develop a dynamic Web page, a component of a distributed application, a database access component, or a classic Windows desktop application. In order to incorporate Visual Basic into the .NET Framework, a number of new features have been added to it. In fact, the changes are so extensive that VB.NET should be viewed as a new language rather than simply as Visual Basic 7. However, these changes were necessary to give developers the features that they have been asking for: true object orientated programming, easier deployment, better interoperability, and a cohesive environment in which to develop applications. What does this book cover? In this book, we cover VB.NET virtually from start to finish: We begin by looking at the .NET Framework, and end by looking at best practices for deploying .NET applications. In between, we look at everything from database access to integration with other technologies such as XML, along with investigating the new features in detail. You will see that VB.NET has emerged as a powerful yet easy to use language that will allow you to target the Internet just as easily as the desktop. This book explains the underlying philosophy and design of the .NET Framework and Common Language Runtime (CLR) and explains the differences between Visual Basic 6 and Visual Basic .NET. You will learn how to Develop applications and components using Visual Studio .NET Effectively apply inheritance and interfaces when designing objects and components Organize your code using namespaces Handle errors using the Try...Catch...Finally structure Access data using ADO.NET and bind controls to the underlying data sources Create Windows applications and custom Windows controls Interoperate with COM and ActiveX components Create transactional and queuing components Use .NET Remoting to send serialized objects between clients and servers Create Windows Services Use VB.NET to access information on the Web Create and consume Web Services Secure your applications and code using the tools provided in the .NET Framework SDK Arrange your applications and libraries in assemblies and deploy them using Visual Studio .NET Who is this book for? This book is aimed at experienced Visual Basic developers who want to make the transition to VB.NET. What do you need to use this book? Although it is possible to create VB.NET applications using the command lines tools contained in the .NET Framework SDK, you will need Visual Studio .NET (Professional or higher), which includes the .NET Framework SDK, to use this book to the full. Here are some additional notes on what you may need: Some chapters make use of SQL Server 2000. However, you can also run the example code using MSDE (Microsoft Data Engine), which ships with Visual Studio .NET. Several chapters make use of Internet Information Services (IIS). IIS ships with Windows 2000 Server, Windows 2000 Professional, and Windows XP, although it is not installed by default. Chapter 18 makes use of MSMQ to work with queued transactions. MSMQ ships with Windows 2000 Server, Windows 2000 Professional, and Windows XP, although it is not installed by default.

Step by Step Guide to OKRs

Step by Step Guide to OKRs
Author: Alexander Maasik
Publisher: Weekdone
Total Pages: 31
Release: 2017-02-02
Genre: Business & Economics
ISBN:

This “Step by Step Guide to OKRs” is a practical guide to goal setting that offers concrete examples to help you start setting impactful and meaningful goals. This book teaches you how to manage a team better and create a feeling of success.

Python Machine Learning

Python Machine Learning
Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
Total Pages: 455
Release: 2015-09-23
Genre: Computers
ISBN: 1783555149

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Introduction to Autonomous Mobile Robots, second edition

Introduction to Autonomous Mobile Robots, second edition
Author: Roland Siegwart
Publisher: MIT Press
Total Pages: 473
Release: 2011-02-18
Genre: Computers
ISBN: 0262295091

The second edition of a comprehensive introduction to all aspects of mobile robotics, from algorithms to mechanisms. Mobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory. The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques. This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners. Curriculum developed by Dr. Robert King, Colorado School of Mines, and Dr. James Conrad, University of North Carolina-Charlotte, to accompany the National Instruments LabVIEW Robotics Starter Kit, are available. Included are 13 (6 by Dr. King and 7 by Dr. Conrad) laboratory exercises for using the LabVIEW Robotics Starter Kit to teach mobile robotics concepts.