Through exposure to the news and social media, you are probably aware of the fact that machine learning has become one of the most exciting technologies of our time and age. Large companies, such as Google, Facebook, Apple, Amazon, and IBM, heavily invest in machine learning research and applications for good reasons. While it may seem that machine learning has become the buzzword of our time and age, it is certainly not a fad. This exciting field opens the way to new possibilities and has become indispensable to our daily lives. This is evident in talking to the voice assistant on our smartphones, recommending the right product for our customers, preventing credit card fraud, filtering out spam from our email inboxes, detecting and diagnosing medical diseases, the list goes on and on. If you want to become a machine learning practitioner, a better problem solver, or maybe even consider a career in machine learning research, then this book is for you. However, for a novice, the theoretical concepts behind machine learning can be quite overwhelming. Many practical books have been published in recent years that will help you get started in machine learning by implementing powerful learning algorithms. Getting exposed to practical code examples and working through example applications of machine learning are a great way to dive into this field. Concrete examples help illustrate the broader concepts by putting the learned material directly into action. However, remember that with great power comes great responsibility! In addition to offering a hands-on experience with machine learning using the Python programming languages and Python-based machine learning libraries, this book introduces the mathematical concepts behind machine learning algorithms, which is essential for using machine learning successfully. Thus, this book is different from a purely practical book; it is a book that discusses the necessary details regarding machine learning concepts and offers intuitive yet informative explanations of how machine learning algorithms work, how to use them, and most importantly, how to avoid the most common pitfalls. Currently, if you type "machine learning" as a search term in Google Scholar, it returns an overwhelmingly large number of publications—1,800,000. Of course, we cannot discuss the nitty-gritty of all the different algorithms and applications that have emerged in the last 60 years. However, in this book, we will embark on an exciting journey that covers all the essential topics and concepts to give you a head start in this field. If you find that your thirst for knowledge is not satisfied, this book references many useful resources that can be used to follow up on the essential breakthroughs in this field. If you have already studied machine learning theory in detail, this book will show you how to put your knowledge into practice. If you have used machine learning techniques before and want to gain more insight into how machine learning actually works, this book is for you. Don't worry if you are completely new to the machine learning field; you have even more reason to be excited. Here is a promise that machine learning will change the way you think about the problems you want to solve and will show you how to tackle them by unlocking the power of data. Before we dive deeper into the machine learning field, let's answer your most important question, "Why Python?" The answer is simple: it is powerful yet very accessible. Python has become the most popular programming language for data science because it allows us to forget about the tedious parts of programming and offers us an environment where we can quickly jot down our ideas and put concepts directly into action. We, the authors, can truly say that the study of machine learning has made us better scientists, thinkers, and problem solvers. In this book, we want to share this knowledge with you. Knowledge is gained by learning. The key is our enthusiasm, and the real mastery of skills can only be achieved by practice. The road ahead may be bumpy on occasions and some topics may be more challenging than others, but we hope that you will embrace this opportunity and focus on the reward. Remember that we are on this journey together, and throughout this book, we will add many powerful techniques to your arsenal that will help us solve even the toughest problems the data-driven way.

Welcome to Learning Node.js Development. This book is packed with a ton of content, projects, challenges and real-world examples, all designed to teach you Node by doing. This means you'll be getting your hands dirty early on in the upcoming chapters writing some code, and you'll be writing code for every project. You will be writing every line of code that powers our applications. Now, we would require a text editor for this book. We have various text editor options that you can use. I always recommend using Atom, which you can find at atom.io. It's free, open-source, and it's available for all operating systems, namely Linux, macOS, and Windows. It's created by the folks behind GitHub. All the projects in the book are fun to build and they were designed to teach you everything required to launch your own Node app, from planning to development and testing to deploying. Now, as you launch these different Node applications and move through the book, you will run into errors, which is bound to happen. Maybe something doesn't get installed as expected, or maybe you try to run an app and instead of getting the expected output, you get a really long obscure error message. Don't worry, I am there to help. I'll show you tips and tricks to get pass through those errors in the chapters. Let's go ahead and get to it.

The mission of the book is to make you familiar with the tools that you can use to develop and deploy Java EE applications in the cloud. You will be led through the whole application development process: creating the application, deploying in the cloud, configuring Continuous Integration, and secure and fault-tolerant communication between the created services. As a result, you will gain practical knowledge of Java EE cloud development, which you can use as a reference for your further projects.

Bayesian statistics has been around for more than 250 years now. During this time it has enjoyed as much recognition and appreciation as disdain and contempt. Through the last few decades it has gained more and more attention from people in statistics and almost all other sciences, engineering, and even outside the walls of the academic world. This revival has been possible due to theoretical and computational developments. Modern Bayesian statistics is mostly computational statistics. The necessity for exible and transparent models and a more interpretation of statistical analysis has only contributed to the trend. Here, we will adopt a pragmatic approach to Bayesian statistics and we will not care too much about other statistical paradigms and their relationship to Bayesian statistics. The aim of this book is to learn about Bayesian data analysis with the help of Python. Philosophical discussions are interesting but they have already been undertaken elsewhere in a richer way than we can discuss in these pages. We will take a modeling approach to statistics, we will learn to think in terms of probabilistic models, and apply Bayes' theorem to derive the logical consequences of our models and data. The approach will also be computational; models will be coded using PyMC3—a great library for Bayesian statistics that hides most of the mathematical details and computations from the user. Bayesian methods are theoretically grounded in probability theory and hence it's no wonder that many books about Bayesian statistics are full of mathematical formulas requiring a certain level of mathematical sophistication. Learning the mathematical foundations of statistics could certainly help you build better models and gain intuition about problems, models, and results. Nevertheless, libraries, such as PyMC3 allow us to learn and do Bayesian statistics with only a modest mathematical knowledge, as you will be able to verify by yourself throughout this book.

Database Publishing Wizard 1.4

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现代雷达原理第三卷，于2014年刚刚出版，完美电子档，在前两卷的基础上，讲述几例典型的雷达系统，是雷达技术综合应用的体现。

使用C ++ Cookbook的虚幻引擎4.x脚本-第二版 这是Packt发布的《 进行的代码库。 使用C ++和UE4的功能开发高质量的游戏组件并解决脚本问题 这本书是关于什么的？ 虚幻引擎4（UE4）是受欢迎且屡获殊荣的游戏引擎，可为某些最受欢迎的游戏提供支持。 一个真正强大的游戏开发工具，再没有比现在更好的时候将其用于商业和独立项目了。 本书提供了100多种食谱，展示了如何在使用虚幻引擎开发游戏时释放C ++的功能。 本书涵盖以下激动人心的功能： 如果您觉得这本书适合您，请立即获取！ 说明和导航 所有代码都组织在文件夹中。 例如，Chapter02。 该代码将如下所示： FString name = "Tim"; int32 mana = 450; FString string = FString::Printf( TEXT( "Name = %s Mana = %d

精通OpenCV 4-第三版 这是Packt发行的的代码库。 使用C ++构建计算机视觉和图像处理应用程序的综合指南 这本书是关于什么的？ 精通OpenCV，目前已是第三版，其目标是计算机视觉工程师朝着精通OpenCV迈出了第一步。 本书将数学公式保持在一个坚实但几乎没有的最低限度，为您提供了完整的项目，从构思到运行代码，针对计算机视觉领域当前的热门话题，例如人脸识别，界标检测和姿势估计以及具有深度卷积网络的数字识别。 本书涵盖以下激动人心的功能： 使用有效的OpenCV代码示例构建实际的计算机视觉问题 发现工程和维护OpenCV项目的最佳实践 探索用于复杂计算机视觉任务的算法设计方法 通过项目使用OpenCV最新API（v4.0.0） 了解Motion的3D场景重建和结构（SfM） 使用ArUco模块研究相机校准和叠加AR 如果您觉得这本书适合您，请立即获取！ 说明和导航

Software-Defined Networking (SDN) with OpenStack-Packt Publishing(2016)