Mastering Machine Learning with R, 2nd Edition-Packt Publishing(2017).epub下载

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It is not so often in life that you get a second chance. I remember that only days after we stopped editing the first edition, I kept asking myself, "Why didn't I...?", or "What the heck was I thinking saying it like that?", and on and on. In fact, the first project I started working on after it was
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It is not so often in life that you get a second chance. I remember that only days after we stopped editing the first edition, I kept asking myself, "Why didn't I...?", or "What the heck was I thinking saying it like that?", and on and on. In fact, the first project I started working on after it was

Linux has been the mainstay of embedded computing for many years. And yet, there are remarkably few books that cover the topic as a whole: this book is intended to fill that gap. The term embedded Linux is not well-defined, and can be applied to the operating system inside a wide range of devices ranging from thermostats to Wi-Fi routers to industrial control units. However, they are all built on the same basic open source software. Those are the technologies that I describe in this book, based on my experience as an engineer and the materials I have developed for my training courses. Technology does not stand still. The industry based around embedded computing is just as susceptible to Moore's law as mainstream computing. The exponential growth that this implies has meant that a surprisingly large number of things have changed since the first edition of this book was published. This second edition is fully revised to use the latest versions of the major open source components, which include Linux 4.9, Yocto Project 2.2 Morty, and Buildroot 2017.02. Since it is clear that embedded Linux will play an important part in the Internet of Things, there is a new chapter on the updating of devices in the field, including Over the Air updates. Another trend is the quest to reduce power consumption, both to extend the battery life of mobile devices and to reduce energy costs. The chapter on power management shows how this is done. Mastering Embedded Linux Programming covers the topics in roughly the order that you will encounter them in a real-life project. The first 6 chapters are concerned with the early stages of the project, covering basics such as selecting the toolchain, the bootloader, and the kernel. At the conclusion of this this section, I introduce the idea of using an embedded build tool, using Buildroot and the Yocto Project as examples. The middle part of the book, chapters 7 through to 13, will help you in the implementation phase of the project. It covers the topics of filesystems, the init program, multithreaded programming, software update, and power management. The third section, chapters 14 and 15, show you how to make effective use of the many debug and profiling tools that Linux has to offer in order to detect problems and identify bottlenecks. The final chapter brings together several threads to explain how Linux can be used in real-time applications. Each chapter introduces a major area of embedded Linux. It describes the background so that you can learn the general principles, but it also includes detailed worked examples that illustrate each of these areas. You can treat this as a book of theory, or a book of examples. It works best if you do both: understand the theory and try it out in real life.

When Apple launched Swift in 2014, the iOS ecosystem became more accessible than it had ever been for developers. Being able to write apps with Swift's beautiful, readable, and straightforward syntax instead of having to write clunky Objective-C code suddenly enabled a lot of people to play around with iOS development. For me, Swift sparked a renewed interest in developing iOS applications, and the new passion has remained ever since. This book will hopefully transfer some of that passion to you. This book is written to match my personal preferred way of learning, which is learning by doing. In this book, you'll find examples of applications that you might actually want to build some day. The problems that are tackled in this book are real and practical. This book doesn't cover every single framework that you can use in your apps; there are simply way too many. Instead, the focus is on understanding how you can navigate Apple's documentation, how you can refactor your code, and how you can solve real-world development problems. If you have tried to build something for iOS before but never really managed to make a full blown app, or if you're looking to expand your knowledge with some iOS-11 specific topics, this book is perfect for you. We'll step beyond the basics and learn everything you need to know to build high-quality applications.

Big data – that was our motivation to explore the world of machine learning with Spark a couple of years ago. We wanted to build machine learning applications that would leverag models trained on large amounts of data, but the beginning was not easy. Spark was still evolving, it did not contain a powerful machine learning library, and we were still trying to figure out what it means to build a machine learning application. But, step by step, we started to explore different corners of the Spark ecosystem and followed Spark’s evolution. For us, the crucial part was a powerful machine learning library, which would provide features such as R or Python libraries did. This was an easy task for us, since we are actively involved in the development of H2O’s machine learning library and its branch called Sparkling Water, which enables the use of the H2O library from Spark applications. However, model training is just the tip of the machine learning iceberg. We still had to explore how to connect Sparkling Water to Spark RDDs, DataFrames, and DataSets, how to connect Spark to different data sources and read data, or how to export models and reuse them in different applications. During our journey, Spark evolved as well. Originally, being a pure Scala project, it started to expose Python and, later, R interfaces. It also took its Spark API on a long journey from low-level RDDs to a high-level DataSet, exposing a SQL-like interface. Furthermore, Spark also introduced the concept of machine learning pipelines, adopted from the scikit-learn library known from Python. All these improvements made Spark a great tool for data transformation and data processing. Based on this experience, we decided to share our knowledge with the rest of the world via this book. Its intention is simple: to demonstrate different aspects of building Spark machine learning applications on examples, and show how to use not only the latest Spark features, but also low-level Spark interfaces. On our journey

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