Build modern day application by implementing Material Design principles in React applications using Material-UI. Key Features * Grasp the needs React components for UI elements * Master the style and theme of the tools to go along with these components * Get to grips with all the React best practices to build a modern application Book Description Material-UI is a component library for rendering UI elements, using modern best practices from React and Material Design. This book will show you how you can create impressive and captivating modern day web apps by implementing Material Design considerations. The primary objective of this book is to help you use several Material-UI components to build larger UI functionality. We will also focus on React best practices in conjunction with Material-UI components – using state, context, and other new React 16.8 features properly. This book will start with layout and navigation and then dive into presenting the information with Material-UI components. It will then talk about the different components for user interactions. By the end of this book, you will know how to improve the look and feel of your applications using Material-UI components. What you will learn * Learn to build the overall structure and navigation for your Material-UI app * Learn to present simple and complex information in a variety of ways * Build interactive and intuitive controls * Design portable themes and styles for all of your Material-UI apps * Group content into sections using tabs and expansion panels * Learn how to design a general page layout with Material-UI grids * Use lists for complex data, and cards for detailed information Who this book is for They are JavaScript developers who have some basic knowledge of React and would want to implement Material Design principles in React applications using Material-UI. The reader wants to build a user interface using React components but doesn’t want to invent their own style or UX framework.

Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features * Study new techniques for marketing analytics * Explore uses of machine learning to power your marketing analyses * Work through each stage of data analytics with the help of multiple examples and exercises Book Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions. What you will learn * Analyze and visualize data in Python using pandas and Matplotlib * Study clustering techniques, such as hierarchical and k-means clustering * Create customer segments based on manipulated data * Predict customer lifetime value using linear regression * Use classification algorithms to understand customer choice * Optimize classification algorithms to extract maximal information Who this book is for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.

Enhance your programming skills to build exciting robotic projects Key Features * Build an intelligent robot that can detect and avoid obstacles and respond to voice commands * Detect and track objects and faces using OpenCV * Control your robot with a GUI button designed using Qt5 Book Description C++ is one of the most popular legacy programming languages for robotics, and a combination of C++ and robotics hardware is used in many leading industries. This book will bridge the gap between Raspberry Pi and C/C++ programming and enable you to develop applications for Raspberry Pi. To follow along with the projects covered in the book, you can implement C programs in Raspberry Pi with the wiringPi library. With this book, you’ll develop a fully functional car robot and write programs to move it in different directions. You’ll then create an obstacle - avoiding robot using an ultrasonic sensor. Furthermore, you’ll find out how to control the robot wirelessly using your PC/Mac. This book will also help you work with object detection and tracking using OpenCV, and guide you through exploring face detection techniques. Finally, you will create an Android app and control the robot wirelessly with an Android smartphone. By the end of this book, you will have gained experience in developing a robot using Raspberry Pi and C/C++ programming. What you will learn * Install software in Raspberry Pi compatible with C++ programming * Program the Raspberry Pi in C++ to run a motor * Control RPi-powered robot wirelessly with your laptop or PC * Program an RPi camera using OpenCV Control a Raspberry Pi robot with voice commands * Implement face and object detection with Raspberry Pi Who this book is for This book is for developers, programmers, and robotics enthusiasts interested in leveraging C++ to build exciting robotics applications. Prior knowledge of C++ is necessary to understand the projects covered in this book.

Design and implement professional level programs by exploring modern data structures and algorithms in Rust. Key Features * Use data structures such as arrays, stacks, trees, lists and graphs with real-world examples * Learn the functional and reactive implementations of the traditional data structures * Explore illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Book Description Rust has come a long way and is now utilized in several contexts. Its key strengths are its software infrastructure and resource-constrained applications, including desktop applications, servers, and performance-critical applications, not forgetting its importance in systems' programming. This book will be your guide as it takes you through implementing classic data structures and algorithms in Rust, helping you to get up and running as a confident Rust programmer. The book begins with an introduction to Rust data structures and algorithms, while also covering essential language constructs. You will learn how to store data using linked lists, arrays, stacks, and queues. You will also learn how to implement sorting and searching algorithms. You will learn how to attain high performance by implementing algorithms to string data types and implement hash structures in algorithm design. The book will examine algorithm analysis, including Brute Force algorithms, Greedy algorithms, Divide and Conquer algorithms, Dynamic Programming, and Backtracking. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. What you will learn * Design and implement complex data structures in Rust * Analyze, implement, and improve searching and sorting algorithms in Rust * Create and use well-tested and reusable components with Rust * Understand the basics of multithreaded programming and advanced algorithm design * Become familiar with application profiling based on benchmarking and testing * Explore the borrowing complexity of implementing algorithms Who this book is for This book is for developers seeking to use Rust solutions in a practical/professional setting; who wants to learn essential Data Structures and Algorithms in Rust. It is for developers with basic Rust language knowledge, some experience in other programming languages is required.

Leverage Elastic Stack’s machine learning features to gain valuable insight from your data Key Features * Combine machine learning with the analytic capabilities of Elastic Stack * Analyze large volumes of search data and gain actionable insight from them * Use external analytical tools with your Elastic Stack to improve its performance Book Description Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly. What you will learn * Install the Elastic Stack to use machine learning features * Understand how Elastic machine learning is used to detect a variety of anomaly types * Apply effective anomaly detection to IT operations and security analytics * Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting * Combine your created jobs to correlate anomalies of different layers of infrastructure * Learn various tips and tricks to get the most out of Elastic machine learning Who this book is for If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.

An in-depth and comprehensive take on WordPress, covering everything about the platform such as WordPress posts, pages, themes, plugins, and more. Key Features * Get up to date with the latest WordPress 5.0 (Bebo) and all its new features * Beginner-friendly layout and advice you can apply from day one with loads of screenshots and examples * Enrich your development experience with the new block-based editor Gutenberg Book Description Back in the day, when you wanted to launch a new website, you either had to learn web programming yourself or hire a professional who would take care of the whole process for you. Nowadays, with WordPress, anyone can build an optimized site with the least amount of effort possible and then make it available to the world in no time. Here, in the seventh edition of the book, we are going to show you how to build great looking and functional websites using WordPress. The new version of WordPress – 5.0 – comes with a few important changes, and we tell you all about how to use them effectively. From crafting content pages using the block editor, and customizing the design of your site, through to making sure it's secure, we go through it all. The book starts by introducing WordPress and teaching you how to set it up. You are then shown how to create a blog site, start writing content, and even use plugins and themes to customize the design of the site and add some unique elements to set it apart. If you want to get more in-depth, we also show you how to get started creating your own themes and plugins. Finally, we teach you how to use WordPress for building non-blog websites. By the end of the book, you will be sufficiently skilled to design high-quality websites and will be fully familiar with the ins and outs of WordPress. What you will learn * Learn to adapt your plugin with the Gutenberg editor * Create content that is optimized for publication on the web * Craft great looking pages and posts with the use of block editor * Structure your web pages in an accessible and clear way * Install and work with plugins and themes * Customize the design of your website * Upload multimedia content, such as images, audio, and video easily and effectively * Develop your own WordPress plugins and themes * Use WordPress to build websites that serve purposes other than blogs Who this book is for The ideal target audience for this book would be PHP developers who have some basic knowledge of working with WordPress and who want to get a comprehensive practical understanding of working with WordPress and create production-ready websites with it.

Unleash the benefits of VMware vSphere 6.7 to provide a powerful, flexible and secure digital infrastructure Key Features * Deep dive into areas like management, security, scalability, availability and more with vSphere 6.7 * Design, deploy and manage VMware vSphere virtual datacenters * Implement monitoring and security of VMware workloads with ease Book Description vSphere 6.7 is the latest release of VMware’s industry-leading, virtual cloud platform. It allows organisations to move to hybrid cloud computing by enabling them to run, manage, connect and secure applications in a common operating environment. This up-to-date, 2nd edition provides complete coverage of vSphere 6.7. Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin with an overview of the products, solutions and features of the vSphere 6.7 suite. You’ll learn how to design and plan a virtual infrastructure and look at the workflow and installation of components. You'll gain insight into best practice configuration, management and security. By the end the book you'll be able to build your own VMware vSphere lab that can run even the most demanding of workloads. What you will learn * Explore the immense functionality of vSphere 6.7 * Design, manage and administer a virtualization environment * Get tips for the VCP6-DCV and VCIX6-DCV exams * Understand how to implement different migration techniques across different environments * Explore vSphere 6.7s powerful capabilities for patching, upgrading and managing the configuration of virtual environments. * Understand core vSphere components * Master resource management, disaster recovery, troubleshooting, monitoring and security Who this book is for This book is for Administrators, Infrastructure Engineers, Architects, and Consultants with basic knowledge of VMware vSphere.

Build effective RESTful APIs for enterprise with design patterns and REST framework’s out-of-the-box capabilities Key Features * Understand advanced topics such as API gateways, API securities, and cloud * Implement patterns programmatically with easy-to-follow examples * Modernize legacy codebase using API connectors, layers, and microservices Book Description This book deals with the Representational State Transfer (REST) paradigm, which is an architectural style that allows networked devices to communicate with each other over the internet. With the help of this book, you’ll explore the concepts of service-oriented architecture (SOA), event-driven architecture (EDA), and resource-oriented architecture (ROA). This book covers why there is an insistence for high-quality APIs toward enterprise integration. It also covers how to optimize and explore endpoints for microservices with API gateways and touches upon integrated platforms and Hubs for RESTful APIs. You’ll also understand how application delivery and deployments can be simplified and streamlined in the REST world. The book will help you dig deeper into the distinct contributions of RESTful services for IoT analytics and applications. Besides detailing the API design and development aspects, this book will assist you in designing and developing production-ready, testable, sustainable, and enterprise-grade APIs. By the end of the book, you’ll be empowered with all that you need to create highly flexible APIs for next-generation RESTful services and applications. What you will learn * Explore RESTful concepts, including URI, HATEOAS, and Code on Demand * Study core patterns like Statelessness, Pagination, and Discoverability * Optimize endpoints for linked microservices with API gateways * Delve into API authentication, authorization, and API security implementations * Work with Service Orchestration to craft composite and process-aware services * Expose RESTful protocol-based APIs for cloud computing Who this book is for This book is primarily for web, mobile, and cloud services developers, architects, and consultants who want to build well-designed APIs for creating and sustaining enterprise-class applications. You’ll also benefit from this book if you want to understand the finer details of RESTful APIs and their design techniques along with some tricks and tips.

Leverage the power of PostgreSQL 11 to build powerful database and data warehousing applications Key Features * Monitor, secure, and fine-tune your PostgreSQL 11 database * Learn client-side and server-side programming using SQL and PL/pgSQL * Discover tips on implementing efficient database solutions Book Description PostgreSQL is one of the most popular open source database management systems in the world, and it supports advanced features included in SQL standards. This book will familiarize you with the latest features in PostgreSQL 11, and get you up and running with building efficient PostgreSQL database solutions from scratch. Learning PostgreSQL, 11 begins by covering the concepts of relational databases and their core principles. You’ll explore the Data Definition Language (DDL) and commonly used DDL commands supported by ANSI SQL. You’ll also learn how to create tables, define integrity constraints, build indexes, and set up views and other schema objects. As you advance, you’ll come to understand Data Manipulation Language (DML) and server-side programming capabilities using PL/pgSQL, giving you a robust background to develop, tune, test, and troubleshoot your database application. The book will guide you in exploring NoSQL capabilities and connecting to your database to manipulate data objects. You’ll get to grips with using data warehousing in analytical solutions and reports, and scaling the database for high availability and performance. By the end of this book, you’ll have gained a thorough understanding of PostgreSQL 11 and developed the necessary skills to build efficient database solutions. What you will learn * Understand the basics of relational databases, relational algebra, and data modeling * Install a PostgreSQL server, create a database, and implement your data model * Create tables and views, define indexes and stored procedures, and implement triggers * Make use of advanced data types such as Arrays, hstore, and JSONB * Connect your Python applications to PostgreSQL and work with data efficiently * Identify bottlenecks to enhance reliability and performance of database applications Who this book is for This book is for you if you're interested in learning about PostgreSQL from scratch. Those looking to build solid database or data warehousing applications or wanting to get up to speed with the latest features of PostgreSQL 11 will also find this book useful. No prior knowledge of database programming or administration is required to get started.

Master the concepts and techniques that will enable you to succeed on the SK0-004 exam the first time with the help of this study guide Key Features * Explore virtualisation, IPv4 & IPv6 networking, administration and more * Enhancing limited knowledge of server configuration and function * A study guide that covers the objectives for the certification examination Book Description CompTIA Server+ Certification is one of the top 5 IT certifications that is vendor neutral.System administrators opt for CompTIA server+ Certification to gain advanced knowledge of concepts including troubleshooting and networking. This book will initially start with the configuration of a basic network server and the configuration for each of its myriad roles. The next set of chapters will provide an overview of the responsibilities and tasks performed by a system administrator to manage and maintain a network server. Moving ahead, you will learn the basic security technologies, methods, and procedures that can be applied to a server and its network. Next, you will cover the troubleshooting procedures and methods in general, and specifically for hardware, software, networks, storage devices, and security applications. Toward the end of this book, we will cover a number of troubleshooting and security mitigation concepts for running admin servers with ease. This guide will be augmented by test questions and mock papers that will help you obtain the necessary certification. By the end of this book, you will be in a position to clear Server+ Certification with ease. What you will learn * Understand the purpose and role of a server in a computer network * Review computer hardware common to network servers * Detail the function and configuration of network operating systems * Describe the functions and tasks of network operating system administration * Explain the various data storage options on a computer network * Detail the need for, and the functioning and application of, network and server security * Describe the operational elements of a network provided by a server * Explain the processes and methods involved in troubleshooting server issues Who this book is for This book is targeted towards professionals seeking to gain the CompTIA Server+ certification. People coming from a Microsoft background with basic operating system and networking skills will also find this book useful. Basic experience working with system administration is mandatory.

Use Python and NLTK (Natural Language Toolkit) to build out your own text classifiers and solve common NLP problems. Key Features * Assimilate key NLP concepts and terminologies * Explore popular NLP tools and techniques * Gain practical experience using NLP in application code Book Description If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language. What you will learn * Obtain, verify, and clean data before transforming it into a correct format for use * Perform data analysis and machine learning tasks using Python * Understand the basics of computational linguistics * Build models for general natural language processing tasks * Evaluate the performance of a model with the right metrics * Visualize, quantify, and perform exploratory analysis from any text data Who this book is for Natural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product. It'll help you to have prior experience of coding in Python using data types, writing functions, and importing libraries. Some experience with linguistics and probability is useful but not necessary.

Design, develop, and solve real-world automation and orchestration problems by unlocking the automation capabilities of Ansible. Key Features * Tackle complex automation challenges with the newly added features in Ansible 2.7 Book Description Automation is essential for success in the modern world of DevOps. Ansible provides a simple, yet powerful, automation engine for tackling complex automation challenges. This book will take you on a journey that will help you exploit the latest version's advanced features to help you increase efficiency and accomplish complex orchestrations. This book will help you understand how Ansible 2.7 works at a fundamental level and will also teach you to leverage its advanced capabilities. Throughout this book, you will learn how to encrypt Ansible content at rest and decrypt data at runtime. Next, this book will act as an ideal resource to help you master the advanced features and capabilities required to tackle complex automation challenges. Later, it will walk you through workflows, use cases, orchestrations, troubleshooting, and Ansible extensions. Lastly, you will examine and debug Ansible operations, helping you to understand and resolve issues. By the end of the book, you will be able to unlock the true power of the Ansible automation engine and tackle complex, real- world actions with ease. What you will learn * Gain an in-depth understanding of how Ansible works under the hood * Fully automate Ansible playbook executions with encrypted data * Access and manipulate variable data within playbooks * Use blocks to perform failure recovery or cleanup * Explore the Playbook debugger and the Ansible Console * Troubleshoot unexpected behavior effectively * Work with cloud infrastructure providers and container systems * Develop custom modules, plugins, and dynamic inventory sources Who this book is for This book is for Ansible developers and operators who have an understanding of its core elements and applications but are now looking to enhance their skills in applying automation using Ansible.

Implement successful private clouds with OpenStack About This Book ? Gain hands-on experience in designing a private cloud for all infrastructures ? Create a robust virtual environment for your organization ? Design, implement and deploy an OpenStack-based cloud based on the Queens release Who This Book Is For OpenStack for Architects is for Cloud architects who are responsible to design and implement a private cloud with OpenStack. System engineers and enterprise architects will also find this book useful. Basic understanding of core OpenStack services, as well as some working experience of concepts, is recommended. What You Will Learn ? Learn the overall structure of an OpenStack deployment ? Craft an OpenStack deployment process which fits within your organization ? Apply Agile Development methodologies to engineer and operate OpenStack clouds ? Build a product roadmap for Infrastructure as a Service based on OpenStack ? Make use of containers to increase the manageability and resiliency of applications running in and on OpenStack. ? Use enterprise security guidelines for your OpenStack deployment In Detail Over the past six years, hundreds of organizations have successfully implemented Infrastructure as a Service (IaaS) platforms based on OpenStack. The huge amount of investment from these organizations, including industry giants such as IBM and HP, as well as open source leaders, such as Red Hat, Canonical, and SUSE, has led analysts to label OpenStack as the most important open source technology since the Linux operating system. Due to its ambitious scope, OpenStack is a complex and fast-evolving open source project that requires a diverse skill set to design and implement it. OpenStack for Architects leads you through the major decision points that you'll face while architecting an OpenStack private cloud for your organization. This book will address the recent changes made in the latest OpenStack release i.e Queens, and will also deal with advanced concepts such as containerization, NVF, and security. At each point, the authors offer you advice based on the experience they've gained from designing and leading successful OpenStack projects in a wide range of industries. Each chapter also includes lab material that gives you a chance to install and configure the technologies used to build production-quality OpenStack clouds. Most importantly, the book focuses on ensuring that your OpenStack project meets the needs of your organization, which will guarantee a successful rollout. Style and approach This is practical, hands-on guide to implementing OpenStack clouds, where each topic is illustrated with real-world examples and then the technical points are proven in the lab. Conceptual chapters are written in discussion style to convey important concepts quickly and present decision points for choosing options.

A step-by-step guide to get you up and running with Azure Networking Services and help you build solutions that leverage effective design patterns About This Book ? Learn best practices for designing and implementing Azure Networking for Azure VMs ? Figure out the hidden secrets to designing a cost-effective environment ? Plan, design, and implement various connectivity scenarios in Azure Who This Book Is For This book is for developers, IT professionals, and database admins who have prior experience of working on Microsoft Azure and want to make the most out of Azure Networking Services. What You Will Learn ? Understand Azure networking and use the right networking service to fulfill your needs ? Design Azure Networks for Azure VMs according to best practices ? Span your environment with Azure networking solutions ? Learn to use Azure DNS ? Implement Azure Load Balancer for highly available environments ? Distribute user traffic across the world via the Azure Traffic Manager ? Control your application delivery with Azure Application Gateway In Detail Microsoft Azure networking is one of the most valuable and important offerings in Azure. No matter what solution you are building for the cloud, you'll find a compelling use for it. This book will get you up to speed quickly on Microsoft Azure Networking by teaching you how to use different networking services. By reading this book, you will develop a strong networking foundation for Azure virtual machines and for expanding your on-premise environment to Azure. Hands-On Networking with Azure starts with an introduction to Microsoft Azure networking and creating Azure Virtual Networks with subnets of different types within them. The book helps you understand the architecture of Azure networks. You will then learn the best practices for designing both Windows- and Linux-based Azure VM networks. You will also learn to expand your networks into Azure and how to use Azure DNS. Moreover, you will master best practices for dealing with Azure Load Balancer and the solutions they offer in different scenarios. Finally, we will demonstrate how the Azure Application Gateway works, offering various layer-7 load balancing capabilities for applications. By the end of this book, you will be able to architect your networking solutions for Azure. Style and approach This book provides in-depth insights into properly designing your environment and saving money on your running Azure Services. Using cutting-edge examples, you will learn to efficiently monitor, diagnose, and troubleshoot Azure Networking

Dive deeper into neural networks and get your models trained, optimized with this quick reference guide About This Book ? A quick reference to all important deep learning concepts and their implementations ? Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more ? Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow. Who This Book Is For If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required. What You Will Learn ? Solve regression and classification challenges with TensorFlow and Keras ? Learn to use Tensor Board for monitoring neural networks and its training ? Optimize hyperparameters and safe choices/best practices ? Build CNN's, RNN's, and LSTM's and using word embedding from scratch ? Build and train seq2seq models for machine translation and chat applications. ? Understanding Deep Q networks and how to use one to solve an autonomous agent problem. ? Explore Deep Q Network and address autonomous agent challenges. In Detail Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks. By the end of this book, you will be able to solve real-world problems quickly with deep neural networks. Style and approach An easy-to-follow, step-by-step guide to help you get to grips with real-world applications of training deep neural networks.

Learn how to solve real life problems using different methods like logic regression, random forests and SVM’s with TensorFlow. About This Book ? Understand predictive analytics along with its challenges and best practices ? Embedded with assessments that will help you revise the concepts you have learned in this book Who This Book Is For This book is aimed at 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. What You Will Learn ? Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration ? Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics ? Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics ? Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations ? Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets In Detail 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. Style and approach This is a fast-paced guide that provides a quick learning solution to all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow Note: This book is a blend of text and quizzes, all packaged up keeping your journey in mind. It includes content from the following Packt product: ? Predictive Analytics with TensorFlow by Md. Rezaul Karim

Text processing and pattern matching simplified About This Book ? Master the fastest and most elegant big data munging language ? Implement text processing and pattern matching using the advanced features of AWK and GAWK ? Implement debugging and inter-process communication using GAWK Who This Book Is For This book is for developers or analysts who are inclined to learn how to do text processing and data extraction in a Unix-like environment. Basic understanding of Linux operating system and shell scripting will help you to get the most out of the book. What You Will Learn ? Create and use different expressions and control flow statements in AWK ? Use Regular Expressions with AWK for effective text-processing ? Use built-in and user-defined variables to write AWK programs ? Use redirections in AWK programs and create structured reports ? Handle non-decimal input, 2-way inter-process communication with Gawk ? Create small scripts to reformat data to match patterns and process texts In Detail AWK is one of the most primitive and powerful utilities which exists in all Unix and Unix-like distributions. It is used as a command-line utility when performing a basic text-processing operation, and as programming language when dealing with complex text-processing and mining tasks. With this book, you will have the required expertise to practice advanced AWK programming in real-life examples. The book starts off with an introduction to AWK essentials. You will then be introduced to regular expressions, AWK variables and constants, arrays and AWK functions and more. The book then delves deeper into more complex tasks, such as printing formatted output in AWK, control flow statements, GNU's implementation of AWK covering the advanced features of GNU AWK, such as network communication, debugging, and inter-process communication in the GAWK programming language which is not easily possible with AWK. By the end of this book, the reader will have worked on the practical implementation of text processing and pattern matching using AWK to perform routine tasks. Style and approach An easy-to-follow, step by step guide which will help you get to grips with real-world applications of AWK programming.

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios About This Book ? Build efficient deep learning pipelines using the popular Tensorflow framework ? Train neural networks such as ConvNets, generative models, and LSTMs ? Includes projects related to Computer Vision, stock prediction, chatbots and more Who This Book Is For This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book. What You Will Learn ? Set up the TensorFlow environment for deep learning ? Construct your own ConvNets for effective image processing ? Use LSTMs for image caption generation ? Forecast stock prediction accurately with an LSTM architecture ? Learn what semantic matching is by detecting duplicate Quora questions ? Set up an AWS instance with TensorFlow to train GANs ? Train and set up a chatbot to understand and interpret human input ? Build an AI capable of playing a video game by itself –and win it! In Detail TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently. Style and approach This book contains 10 unique, end-to-end projects covering all aspects of deep learning and their implementations with TensorFlow. Each project will equip you with a unique skillset in training efficient deep learning models, and empower you to implement your own projects more confidently

Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use. About This Book ? Analyze your data using R – the most powerful statistical programming language ? Learn how to implement applied statistics using practical use-cases ? Use popular R packages to work with unstructured and structured data Who This Book Is For Budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in R will find this book to be useful. No prior exposure to data analysis is needed, although a fundamental understanding of the R programming language is required to get the best out of this book. What You Will Learn ? Gain a thorough understanding of statistical reasoning and sampling theory ? Employ hypothesis testing to draw inferences from your data ? Learn Bayesian methods for estimating parameters ? Train regression, classification, and time series models ? Handle missing data gracefully using multiple imputation ? Identify and manage problematic data points ? Learn how to scale your analyses to larger data with Rcpp, data.table, dplyr, and parallelization ? Put best practices into effect to make your job easier and facilitate reproducibility In Detail Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst. Style and approach An easy-to-follow step by step guide which will help you get to grips with real world application of Data Analysis with R

Leverage the power of Matplotlib to visualize and understand your data more effectively About This Book ? Perform effective data visualization with Matplotlib and get actionable insights from your data ? Design attractive graphs, charts, and 2D plots, and deploy them to the web ? Get the most out of Matplotlib in this practical guide with updated code and examples Who This Book Is For This book is essentially for anyone who wants to create intuitive data visualizations using the Matplotlib library. If you’re a data scientist or analyst and wish to create attractive visualizations using Python, you’ll find this book useful. Some knowledge of Python programming is all you need to get started. What You Will Learn ? Create 2D and 3D static plots such as bar charts, heat maps, and scatter plots ? Get acquainted with GTK+3, Qt5, and wxWidgets to understand the UI backend of Matplotlib ? Develop advanced static plots with third-party packages such as Pandas, GeoPandas, and Seaborn ? Create interactive plots with real-time updates ? Develop web-based, Matplotlib-powered graph visualizations with third-party packages such as Django ? Write data visualization code that is readily expandable on the cloud platform In Detail Python is a general-purpose programming language increasingly being used for data analysis and visualization. Matplotlib is a popular data visualization package in Python used to design effective plots and graphs. This is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library. Matplotlib for Python Developers, Second Edition shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you’ll embed and customize your plots in third-party tools such as GTK+3, Qt 5, and wxWidgets. You’ll also be able to tweak the look and feel of your visualization with the help of practical examples provided in this book. Further on, you’ll explore Matplotlib 2.1.x on the web, from a cloud-based platform using third-party packages such as Django. Finally, you will integrate interactive, real-time visualization techniques into your current workflow with the help of practical real-world examples. By the end of this book, you’ll be thoroughly comfortable with using the popular Python data visualization library Matplotlib 2.1.x and leveraging its power to build attractive, insightful, and powerful visualizations. Style and approach Step by step approach to learning the best of Matplotlib 2.1.x

Automate data and model pipelines for faster machine learning applications About This Book ? Build automated modules for different machine learning components ? Understand each component of a machine learning pipeline in depth ? Learn to use different open source AutoML and feature engineering platforms Who This Book Is For If you’re a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you. You’ll also find this book useful if you’re an ML engineer or data professional interested in developing quick machine learning pipelines for your projects. Prior exposure to Python programming will help you get the best out of this book. What You Will Learn ? Understand the fundamentals of Automated Machine Learning systems ? Explore auto-sklearn and MLBox for AutoML tasks ? Automate your preprocessing methods along with feature transformation ? Enhance feature selection and generation using the Python stack ? Assemble individual components of ML into a complete AutoML framework ? Demystify hyperparameter tuning to optimize your ML models ? Dive into Machine Learning concepts such as neural networks and autoencoders ? Understand the information costs and trade-offs associated with AutoML In Detail AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions. Style and approach Step by step approach to understand how to automate your machine learning tasks