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 published had nothing to do with any of the methods in the first edition. I made a mental note that if given the chance, it would go into a second edition. When I started with the first edition, my goal was to create something different, maybe even create a work that was a pleasure to read, given the constraints of the topic. After all the feedback I received, I think I hit the mark. However, there is always room for improvement, and if you try and be everything to all people, you become nothing to everybody. I'm reminded of one of my favorite Frederick the great quotes, "He who defends everything, defends nothing". So, I've tried to provide enough of the skills and tools, but not all of them, to get a reader up and running with R and machine learning as quickly and painlessly as possible. I think I've added some interesting new techniques that build on what was in the first edition. There will probably always be the detractors who complain it does not offer enough math or does not do this, that, or the other thing, but my answer to that is they already exist! Why duplicate what was already done, and very well, for that matter? Again, I have sought to provide something different, something that would keep the reader's attention and allow them to succeed in this competitive field. Before I provide a list of the changes/improvements incorporated into the second edition, chapter by chapter, let me explain some universal changes. First of all, I have surrendered in my effort to fight the usage of the assignment operator <- versus just using =. As I shared more and more code with others, I realized I was out on my own using = and not <-. The first thing I did when under con

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Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement. I believe that any machine learning practitioner should be proficient in statistics as well as in mathematics, so that they can speculate and solve any machine learning problem in an efficient manner. In this book, we will cover the fundamentals of statistics and machine learning, giving you a holistic view of the application of machine learning techniques for relevant problems. We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming. We will use libraries such as scikit-learn, e1071, randomForest, c50, xgboost, and so on. We will also go over the fundamentals of deep learning with the help of Keras software. Furthermore, we will have an overview of reinforcement learning with pure Python programming language. The book is motivated by the following goals: To help newbies get up to speed with various fundamentals, whilst also allowing experienced professionals to refresh their knowledge on various concepts and to have more clarity when applying algorithms on their chosen data. To give a holistic view of both Python and R, this book will take you through various examples using both languages. To provide an introduction to new trends in machine learning, fundamentals of deep learning and reinforcement learning are covered with suitable examples to teach you state of the art techniques.

Mastering TensorFlow 1.x_Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras-Packt Publishing(2018) Google’s TensorFlow has become a major player and a go-to tool for developers to bring smart processing within an application. TensorFlow has become a major research and engineering tool in every organization. Thus, there is a need to learn advanced use cases of TensorFlow that can be implemented in all kinds of software and devices to build intelligent systems. TensorFlow is one of its kind, with lots of new updates and bug fixes to bring smart automation into your projects. So in today’s world, it becomes a necessity to master TensorFlow in order to create advanced machine learning and deep learning applications. Mastering TensorFlow will help you learn all the advanced features TensorFlow has to offer. This book funnels down the key information to provide the required expertise to the readers to enter the world of artificial intelligence, thus extending the knowledge of intermediate TensorFlow users to the next level. From implementing advanced computations to trending real-world research areas, this book covers it all. Get to the grips with this highly comprehensive guide to make yourself well established in the developer community, and you'll have a platform to contribute to research works or projects.

Spring 5 Design Patterns is for all Java developers who want to learn Spring for the enterprise application. Therefore, enterprise Java developers will find it particularly useful in the understanding of design patterns used by the Spring Framework and how it solves common design problems in the enterprise application, and they will fully appreciate the examples presented in this book. Before reading this book, readers should have basic knowledge of Core Java, JSP, Servlet, and XML. Spring 5 Framework is newly launched by Pivotal with reactive programming. Spring 5 introduces many new features and enhancements from its previous version. We will discuss all this in the book. Spring 5 Design Patterns will give you in-depth insight about the Spring Framework. The great part of today's Spring Framework is that all companies have already taken it as a primary framework for development of the enterprise application. For Spring, no external enterprise server is needed to start working with it. The goals of writing this book are to discuss all design patterns used behind the Spring Framework and how they are implemented in the Spring Framework. Here, the author has also given you some best practices that must be used in the design and development of the application. The book contains 12 chapters that cover everything from the basics to more complex design pattern such as reactive programming. Spring 5 Design Patterns is divided into three sections. The first section introduces you to the essentials of the design patterns and the Spring Framework. The second section steps behind the front end and shows where Spring fits in the back end of an application. The third section expands on this by showing how to build web applications with Spring and introducing a new feature of the Spring 5 reactive programming. This part also shows how to handle concurrency in the enterprise application.

Key Features Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples;

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