TensorFlow: Powerful Predictive Analytics with TensorFlow. Predict valuable insights of your data with TensorFlow

Md. Rezaul Karim

Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google’s brainchild, is immensely popular and extensively used for predictive analysis.This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. This book is repurposed for this specific learning experience from material from Packt's Predictive Analytics with TensorFlow by Md. Rezaul Karim.

Spatial Analytics with ArcGIS. Build powerful insights with spatial analytics

Eric Pimpler

Spatial statistics has the potential to provide insight that is not otherwise available through traditional GIS tools. This book is designed to introduce you to the use of spatial statistics so you can solve complex geographic analysis.The book begins by introducing you to the many spatial statistics tools available in ArcGIS. You will learn how to analyze patterns, map clusters, and model spatial relationships with these tools. Further on, you will explore how to extend the spatial statistics tools currently available in ArcGIS, and use the R programming language to create custom tools in ArcGIS through the ArcGIS Bridge using real-world examples. At the end of the book, you will be presented with two exciting case studies where you will be able to practically apply all your learning to analyze and gain insights into real estate data.

ROS Robotics By Example. Learning to control wheeled, limbed, and flying robots using ROS Kinetic Kame - Second Edition

Carol Fairchild, Dr. Thomas L. Harman

ROS is a robust robotics framework that works regardless of hardware architecture or hardware origin. It standardizes most layers of robotics functionality from device drivers to process control and message passing to software package management. But apart from just plain functionality, ROS is a great platform to learn about robotics itself and to simulate, as well as actually build, your first robots. This does not mean that ROS is a platform for students and other beginners; on the contrary, ROS is used all over the robotics industry to implement flying, walking and diving robots, yet implementation is always straightforward, and never dependent on the hardware itself.ROS Robotics has been the standard introduction to ROS for potential professionals and hobbyists alike since the original edition came out; the second edition adds a gradual introduction to all the goodness available with the Kinetic Kame release.By providing you with step-by-step examples including manipulator arms and flying robots, the authors introduce you to the new features. The book is intensely practical, with space given to theory only when absolutely necessary. By the end of this book, you will have hands-on experience on controlling robots with the best possible framework.

Pentaho 8 Reporting for Java Developers. Create pixel-perfect analytical reports using reporting tools

Francesco Corti

This hands-on tutorial, filled with exercises and examples, introduces the reader to a variety of concepts within Pentaho Reporting. With screenshots that show you how reports look at design time as well as how they should look when rendered as PDF, Excel, HTML, Text, Rich-Text-File, XML, and CSV, this book also contains complete example source code that you can copy and paste into your environment to get up-and-running quickly. Updated to cover the features of Pentaho 8, this book will teach you everything you need to know to build fast, efficient reports using Pentaho. If your interest lies in the technical details of creating reports and you want to see how to solve common reporting problems with a minimum of fuss, this is the book for you.

Hands-On Machine Learning for Algorithmic Trading. Design and implement investment strategies based on smart algorithms that learn from data using Python

Stefan Jansen

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies.This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You’ll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies.Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.

Java 9: Building Robust Modular Applications. Master advanced Java features and implement them to build amazing projects

Dr. Edward Lavieri, Peter Verhas, Jason Lee

Java 9 and its new features add to the richness of the language; Java is one of the languages most used by developers to build robust software applications. Java 9 comes with a special emphasis on modularity with its integration with Jigsaw. This course is your one-stop guide to mastering the language.You'll be provided with an overview and explanation of the new features introduced in Java 9 and the importance of the new APIs and enhancements. Some new features of Java 9 are ground-breaking; if you are an experienced programmer, you will be able to make your enterprise applications leaner by learning these new features. You'll be provided with practical guidance in applying your newly acquired knowledge of Java 9 and further information on future developments of the Java platform. This course will improve your productivity, making your applications faster. Next, you'll go on to implement everything you've learned by building 10 cool projects. You will learn to build an email filter that separates spam messages from all your inboxes, a social media aggregator app that will help you efficiently track various feeds, and a microservice for a client/server note application, to name just a few.By the end of this course, you will be well acquainted with Java 9 features and able to build your own applications and projects.This Learning Path contains the best content from the following two recently published Packt products:• Mastering Java 9• Java 9 Programming Blueprints

Hands-On Deep Learning for Images with TensorFlow. Build intelligent computer vision applications using TensorFlow and Keras

Will Ballard

TensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks.Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow.By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.

Swift 3 Object-Oriented Programming. Implement object-oriented programming paradigms with Swift 3.0 and mix them with modern functional programming techniques to build powerful real-world applications - Second Edition

Gaston C. Hillar

Swift has quickly become one of the most-liked languages and developers’ de-facto choice when building applications that target iOS and macOS. In the new version, the Swift team wants to take its adoption to the next level by making it available for new platforms and audiences.This book introduces the object-oriented paradigm and its implementation in the Swift 3 programming language to help you understand how real-world objects can become part of fundamental reusable elements in the code. This book is developed with XCode 8.x and covers all the enhancements included in Swift 3.0. In addition, we teach you to run most of the examples with the Swift REPL available on macOS and Linux, and with a Web-based Swift sandbox developed by IBM capable of running on any web browser, including Windows and mobile devices.You will organize data in blueprints that generate instances. You’ll work with examples so you understand how to encapsulate and hide data by working with properties and access control. Then, you’ll get to grips with complex scenarios where you use instances that belong to more than one blueprint. You’ll discover the power of contract programming and parametric polymorphism. You’ll combine generic code with inheritance and multiple inheritance. Later, you’ll see how to combine functional programming with object-oriented programming and find out how to refactor your existing code for easy maintenance.