This is a book for Business Analysts (BAs) who need to develop a process model for implementation in a business process management system. Developers looking at the JBoss jBPM toolset will also find it a useful introduction to the key concepts. This book is a full toolkit for someone who wants to implement BPM in the right way. This toolkit is particularly aimed at Business Analysts, although Project Managers, IT managers, developers, and even business people can expect to find useful tools and techniques in here. We will present the project framework, analysis techniques and templates, BPM technology and example deliverables that you need to successfully bring a BPM solution into your organization.

Microsoft .NET Framework 4.5 First Look Cookbook

Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. This is a Packt Instant guide, which provides concise and clear recipes to create PLC programs using RSLogix 5000.The purpose of this book is to capture the core elements of PLC programming with RSLogix 5000 so that electricians, instrumentation techs, automation professionals, and students who are familiar with basic PLC programming techniques can come up to speed with a minimal investment of time and energy.

Leverage benefits of machine learning techniques using Python About This Book Improve and optimise machine learning systems using effective strategies. Develop a strategy to deal with a large amount of data. Use of Python code for implementing a range of machine learning algorithms and techniques. Who This Book Is For This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts. What You Will Learn Learn to write clean and elegant Python code that will optimize the strength of your algorithms Uncover hidden patterns and structures in data with clustering Improve accuracy and consistency of results using powerful feature engineering techniques Gain practical and theoretical understanding of cutting-edge deep learning algorithms Solve unique tasks by building models Get grips on the machine learning design process In Detail Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering. Style and approach This course includes all the resources that will help you jump into the data science field with Python. The aim is to walk through the elements of Python covering powerful machine learning libraries. This course will explain important machine learning models in a step-by-step manner. Each topic is well explained with real-world applications with detailed guidance.Through this comprehensive guide, you will be able to explore machine learning techniques.

Leverage the power of Python and its de facto GUI framework to build highly interactive interfaces About This Book ? The fundamentals of Python and GUI programming with Tkinter. ? Create multiple cross-platform projects by integrating a host of third-party libraries and tools. ? Build beautiful and highly-interactive user interfaces that target multiple devices. Who This Book Is For This book is for beginners to GUI programming who haven’t used Tkinter yet and are eager to start building great-looking and user-friendly GUIs. Prior knowledge of Python programming is expected. What You Will Learn ? Create a scrollable frame via theCanvas widget ? Use the pack geometry manager andFrame widget to control layout ? Learn to choose a data structurefor a game ? Group Tkinter widgets, such asbuttons, canvases, and labels ? Create a highly customizablePython editor ? Design and lay out a chat window In Detail Tkinter is a modular, cross-platform application development toolkit for Python. When developing GUI-rich applications, the most important choices are which programming language(s) and which GUI framework to use. Python and Tkinter prove to be a great combination. This book will get you familiar with Tkinter by having you create fun and interactive projects. These projects have varying degrees of complexity. We'll start with a simple project, where you'll learn the fundamentals of GUI programming and the basics of working with a Tkinter application. After getting the basics right, we'll move on to creating a project of slightly increased complexity, such as a highly customizable Python editor. In the next project, we'll crank up the complexity level to create an instant messaging app. Toward the end, we'll discuss various ways of packaging our applications so that they can be shared and installed on other machines without the user having to learn how to install and run Python programs. Style and approach Step by Step guide with real world examples

Teach your machine to think for itself! Key Features * Delve into supervised learning and grasp how a machine learns from data * Implement popular machine learning algorithms from scratch, developing a deep understanding along the way * Explore some of the most popular scientific and mathematical libraries in the Python language Book Description Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems. What you will learn * Crack how a machine learns a concept and generalize its understanding to new data * Uncover the fundamental differences between parametric and non-parametric models * Implement and grok several well-known supervised learning algorithms from scratch * Work with models in domains such as ecommerce and marketing * Expand your expertise and use various algorithms such as regression, decision trees, and clustering * Build your own models capable of making predictions * Delve into the most popular approaches in deep learning such as transfer learning and neural networks Who this book is for This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.

Discover interesting recipes to help you understand the concepts of object detection, image processing, and facial detection Key Features * Explore the latest features and APIs in OpenCV 4 and build computer vision algorithms * Develop effective, robust, and fail-safe vision for your applications * Build computer vision algorithms with machine learning capabilities Book Description OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work through recipes that implement a variety of tasks, such as facial recognition and detection. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs. This book begins by setting up OpenCV, and explains how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection. By the end of the book, you'll be able to confidently implement a range to computer vision algorithms to meet the technical requirements of your complex CV projects What you will learn * Install and create a program using the OpenCV library * Segment images into homogenous regions and extract meaningful objects * Apply image filters to enhance image content * Exploit image geometry to relay different views of a pictured scene * Calibrate the camera from different image observations * Detect people and objects in images using machine learning techniques * Reconstruct a 3D scene from images * Explore face detection using deep learning Who this book is for If you’re a CV developer or professional who already uses or would like to use OpenCV for building computer vision software, this book is for you. You’ll also find this book useful if you’re a C++ programmer looking to extend your computer vision skillset by learning OpenCV.

Java: Beginner's Guide to Programming Code with Java

Exploit design, deployment, and management of large-scale containers About This Book ? Explore the latest features available in Kubernetes 1.10 ? Ensure that your clusters are always available, scalable, and up to date ? Master the skills of designing and deploying large clusters on various cloud platforms Who This Book Is For Mastering Kubernetes is for you if you are a system administrator or a developer who has an intermediate understanding of Kubernetes and wish to master its advanced features. Basic knowledge of networking would also be helpful. In all, this advanced-level book provides a smooth pathway to mastering Kubernetes. What You Will Learn ? Architect a robust Kubernetes cluster for long-time operation ? Discover the advantages of running Kubernetes on GCE, AWS, Azure, and bare metal ? Understand the identity model of Kubernetes, along with the options for cluster federation ? Monitor and troubleshoot Kubernetes clusters and run a highly available Kubernetes ? Create and configure custom Kubernetes resources and use third-party resources in your automation workflows ? Enjoy the art of running complex stateful applications in your container environment ? Deliver applications as standard packages In Detail Kubernetes is an open source system that is used to automate the deployment, scaling, and management of containerized applications. If you are running more containers or want automated management of your containers, you need Kubernetes at your disposal. To put things into perspective, Mastering Kubernetes walks you through the advanced management of Kubernetes clusters. To start with, you will learn the fundamentals of both Kubernetes architecture and Kubernetes design in detail. You will discover how to run complex stateful microservices on Kubernetes including advanced features such as horizontal pod autoscaling, rolling updates, resource quotas, and persistent storage backend. Using real-world use cases, you will explore the options for network configuration, and understand how to set up, operate, and troubleshoot various Kubernetes networking plugins. In addition to this, you will get to grips with custom resource development and utilization in automation and maintenance workflows. To scale up your knowledge of Kubernetes, you will encounter some additional concepts based on the Kubernetes 1.10 release, such as Promethus, Role-based access control, API aggregation, and more. By the end of this book, you’ll know everything you need to graduate from intermediate to advanced level of understanding Kubernetes. Style and approach Delving into the design of the Kubernetes platform, the reader will be exposed to Kubernetes advanced features and best practices. This advanced-level book will provide a pathway to mastering Kubernetes.

Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch Key Features * Learn and implement machine learning algorithms in a variety of real-life scenarios * Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques * Find easy-to-follow code solutions for tackling common and not-so-common challenges Book Description This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples. What you will learn * Use predictive modeling and apply it to real-world problems * Explore data visualization techniques to interact with your data * Learn how to build a recommendation engine * Understand how to interact with text data and build models to analyze it * Work with speech data and recognize spoken words using Hidden Markov Models * Get well versed with reinforcement learning, automated ML, and transfer learning * Work with image data and build systems for image recognition and biometric face recognition * Use deep neural networks to build an optical character recognition system Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.

The book uses stepbystep instructions along with full code listings for each exercise. After each exercise, the author pauses to reflect, explain, and offer insights before building on the project. The author approaches the content with the belief that we are all teachers and that you are reading this book not only because you want to learn, but because you want to share your knowledge with others. Motivated students can pick up this book and teach themselves how to program because the book takes a simple, strategic, and structured approach to learning Scratch. Parents can grasp the fundamentals so that they can guide their children through introductory Scratch programming exercises. It’s perfect for homeschool families. Teachers of all disciplines from computer science to English can quickly get up to speed with Scratch and adapt the projects for use in the classroom.

Over 79 hands-on recipes for professional embedded Linux developers to optimize and boost their Yocto Project know-how About This Book ? Optimize your Yocto setup to speed up development and debug build issues ? Use what is quickly becoming the standard embedded Linux product builder framework—the Yocto Project ? Recipe-based implementation of best practices to optimize your Linux system Who This Book Is For If you are an embedded Linux developer with the basic knowledge of Yocto Project, this book is an ideal way to broaden your knowledge with recipes for embedded development. What You Will Learn ? Optimize your Yocto Project setup to speed up development and debug build issues ? Use Docker containers to build Yocto Project-based systems ? Take advantage of the user-friendly Toaster web interface to the Yocto Project build system ? Build and debug the Linux kernel and its device trees ? Customize your root filesystem with already-supported and new Yocto packages ? Optimize your production systems by reducing the size of both the Linux kernel and root filesystems ? Explore the mechanisms to increase the root filesystem security ? Understand the open source licensing requirements and how to comply with them when cohabiting with proprietary programs ? Create recipes, and build and run applications in C, C++, Python, Node.js, and Java In Detail The Yocto Project has become the de facto distribution build framework for reliable and robust embedded systems with a reduced time to market. You'll get started by working on a build system where you set up Yocto, create a build directory, and learn how to debug it. Then, you'll explore everything about the BSP layer, from creating a custom layer to debugging device tree issues. In addition to this, you’ll learn how to add a new software layer, packages, data, *s, and configuration files to your system. You will then cover topics based on application development, such as using the Software Development Kit and how to use the Yocto project in various development environments. Toward the end, you will learn how to debug, trace, and profile a running system. This second edition has been updated to include new content based on the latest Yocto release. Style and approach This recipe-based book will guide you through all the development stages of an embedded Linux product design using the Yocto Project.

Java: Tips and Tricks to Programming Code with Java

This book is designed for advanced Lumion users who wish to apply these techniques to their own projects. The reader should be familiar with modeling knowledge and have at least a basic knowledge of a graphics editing program, such as Photoshop or Gimp.

This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R.

If you are a system administrator who has some experience with virtualization and already uses VMware vCenter, but wishes to learn more, then this is the book for you. If you are looking for tips or shortcuts for common administration tasks as well as workarounds for pain points in vSphere administration, you'll find this guide useful.

This book is perfect for beginners who want to get started and learn the web development basics, but also offers experienced developers a web development roadmap that will help them to extend their capabilities.

This book is for intermediate Solr Developers who are willing to learn and implement Pro-level practices, techniques, and solutions. This edition will specifically appeal to developers who wish to quickly get to grips with the changes and new features of Apache Solr 5.

If you have just started your adventure with F#, then this book will help you take the right steps to become a successful F# coder. An intermediate knowledge of imperative programming concepts, and a basic understanding of the algorithms and data structures in .NET environments using the C# language and BCL (Base Class Library), would be helpful.

If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.

If you are a JavaScript developer and already know the basics of KnockoutJS and you want to get the most out of it, then this book is for you. This book will help in your transition from a small site to a large web application that is easily maintainable.