XSLT Jumpstarter
Finally, a ground-up, quick-start approach to XSLT that teaches not just the language, but XML processing solutions. XSLT Jumpstarter approaches the subject like no other book, using examples that ease you through the basic concepts while demonstrating how to solve common problems. It doesn't unload language elements on you, it shows how to create HTML output, rearrange and modify XML nodes, manipulate text, conditionalize processing, make global changes, perform grouping and sorting, and implement strategies for re-using templates and stylesheets. XSLT Jumpstarter offers a hands-on, jump-in-the-water approach that will launch you over the XSLT learning curve! ...
Finally, a ground-up, quick-start approach to XSLT that teaches not just the language, but XML processing solutions. XSLT Jumpstarter approaches the subject like no other book, using examples that ease you through the basic concepts while demonstrating how to solve common problems. It doesn't unload language elements on you, it shows how to create HTML output, rearrange and modify XML nodes, manipulate text, conditionalize processing, make global changes, perform grouping and sorting, and implement strategies for re-using templates and stylesheets. XSLT Jumpstarter offers a hands-on, jump-in-the-water approach that will launch you over the XSLT learning curve! ...
Beginning DAX with Power BI
Attention all SQL Pros, DAX is not just for writing Excel-based formulas! Get hands-on learning and expert advice on how to use the vast capabilities of the DAX language to solve common data modeling challenges. Beginning DAX with Power BI teaches key concepts such as mapping techniques from SQL to DAX, filtering, grouping, joining, pivoting, and using temporary tables, all aimed at the SQL professional. Join author Philip Seamark as he guides you on a journey through typical business data transformation scenarios and challenges, and teaches you, step-by-step, how to resolve challenges using DAX. Tips, tricks, and shortcuts are included and explained, along with examples of the SQL equivalent, in order to accelerate learning. Examples in the book range from beginner to advanced, with plenty of detailed explanation when walking through each scenario. Turbocharge your Power BI model by adding advanced DAX programming techniques; Know when to use calculated measures versus calculate ...
Attention all SQL Pros, DAX is not just for writing Excel-based formulas! Get hands-on learning and expert advice on how to use the vast capabilities of the DAX language to solve common data modeling challenges. Beginning DAX with Power BI teaches key concepts such as mapping techniques from SQL to DAX, filtering, grouping, joining, pivoting, and using temporary tables, all aimed at the SQL professional. Join author Philip Seamark as he guides you on a journey through typical business data transformation scenarios and challenges, and teaches you, step-by-step, how to resolve challenges using DAX. Tips, tricks, and shortcuts are included and explained, along with examples of the SQL equivalent, in order to accelerate learning. Examples in the book range from beginner to advanced, with plenty of detailed explanation when walking through each scenario. Turbocharge your Power BI model by adding advanced DAX programming techniques; Know when to use calculated measures versus calculate ...
Advanced Data Analytics Using Python
Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You'll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You'll get to know the concepts using Python code, giving you samples to use in your own projects. Work with data analysis techniques such as classification, clustering, regression, and forecasting; Handle structured and unstructured data, ETL techniques, and different kinds of databases such a ...
Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You'll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You'll get to know the concepts using Python code, giving you samples to use in your own projects. Work with data analysis techniques such as classification, clustering, regression, and forecasting; Handle structured and unstructured data, ETL techniques, and different kinds of databases such a ...
Deep Learning with TensorFlow, 2nd Edition
Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived 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 v1.7, combined with other open source Python libraries. Throughout the book, you'll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the ...
Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived 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 v1.7, combined with other open source Python libraries. Throughout the book, you'll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the ...
Data Science on the Google Cloud Platform
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. Automate and schedule data ingest, using an App Engine application; Create and populate a dashboard in Google Data Studio; Build a real-time analysis pipeline to carry out streaming analytics; Conduct interactive data exploration with Google BigQuery; Create a Bayesian model on a Cloud Dataproc cluster; ...
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. Automate and schedule data ingest, using an App Engine application; Create and populate a dashboard in Google Data Studio; Build a real-time analysis pipeline to carry out streaming analytics; Conduct interactive data exploration with Google BigQuery; Create a Bayesian model on a Cloud Dataproc cluster; ...
Vue.js: Up and Running
Get a brisk introduction to building fast, interactive single-page web applications with Vue.js, the popular JavaScript framework that organizes and simplifies web development. With this practical guide, you'll quickly move from basics to custom components and advanced features - including JSX, the JavaScript syntax extension. Author Callum Macrae shows you how to use the most useful libraries in the Vue ecosystem, such as vue-router for routing, vuex for state management, and vue-test-utils for testing. If you're a frontend developer familiar with JavaScript, HTML, and CSS, this book will show you how to develop a fully featured web application using Vue. Learn Vue.js basics, including its use of templates to display data on a page; Set up Vue projects from scratch, or use vue-cli to set up from a template; Create a maintainable codebase by splitting code into self-contained components; Discover how Vue.js works with CSS to style your websites and applications; Use render functi ...
Get a brisk introduction to building fast, interactive single-page web applications with Vue.js, the popular JavaScript framework that organizes and simplifies web development. With this practical guide, you'll quickly move from basics to custom components and advanced features - including JSX, the JavaScript syntax extension. Author Callum Macrae shows you how to use the most useful libraries in the Vue ecosystem, such as vue-router for routing, vuex for state management, and vue-test-utils for testing. If you're a frontend developer familiar with JavaScript, HTML, and CSS, this book will show you how to develop a fully featured web application using Vue. Learn Vue.js basics, including its use of templates to display data on a page; Set up Vue projects from scratch, or use vue-cli to set up from a template; Create a maintainable codebase by splitting code into self-contained components; Discover how Vue.js works with CSS to style your websites and applications; Use render functi ...
Introduction to Machine Learning with R
Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you'll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods. Finally, you'll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning. Explore machine learning models, algorithms, and data training; Understand machine learning algorithms for supervised and unsupervised cases; Examine statistical concepts for designing data for use in models; Dive into linear regres ...
Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you'll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods. Finally, you'll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning. Explore machine learning models, algorithms, and data training; Understand machine learning algorithms for supervised and unsupervised cases; Examine statistical concepts for designing data for use in models; Dive into linear regres ...
Vuex Quick Start Guide
State management preserves the state of controls in a user interface. Vuex is a state management tool for Vue.js that makes the architecture easier to understand, maintain and evolve. This book is the easiest way to get started with Vuex to improve your Vue.js application architecture and overall user experience. Our book begins by explaining the problem that Vuex solves, and how it helps your applications. You will learn about the Vuex core concepts, including the Vuex store, changing application state, carrying out asynchronous operations and persisting state changes, all with an eye to scalability. You will learn how to test Vuex elements and Vue components with the Karma and Jasmine testing frameworks. You will see this in the context of a testing first approach, following the fundamentals of Test Driven Development. TDD will help you to identify which components need testing and how to test them. You will build a full Vuex application by creating the application components a ...
State management preserves the state of controls in a user interface. Vuex is a state management tool for Vue.js that makes the architecture easier to understand, maintain and evolve. This book is the easiest way to get started with Vuex to improve your Vue.js application architecture and overall user experience. Our book begins by explaining the problem that Vuex solves, and how it helps your applications. You will learn about the Vuex core concepts, including the Vuex store, changing application state, carrying out asynchronous operations and persisting state changes, all with an eye to scalability. You will learn how to test Vuex elements and Vue components with the Karma and Jasmine testing frameworks. You will see this in the context of a testing first approach, following the fundamentals of Test Driven Development. TDD will help you to identify which components need testing and how to test them. You will build a full Vuex application by creating the application components a ...
Fundamentals of Deep Learning
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that's paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you're familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks; Learn how to train feed-forward neural networks; Use TensorFlow to implement your first neural network; Manage problems that arise as you begin to make networks deeper; Build neural networks that analyze complex images; Perform effective dimensionali ...
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that's paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you're familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks; Learn how to train feed-forward neural networks; Use TensorFlow to implement your first neural network; Manage problems that arise as you begin to make networks deeper; Build neural networks that analyze complex images; Perform effective dimensionali ...
Deep Learning
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning - especially deep neural networks - make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular; Understand how deep networks evolved from neural network fundamentals; Explore the major deep network architectures, including Convolutional and R ...
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning - especially deep neural networks - make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular; Understand how deep networks evolved from neural network fundamentals; Explore the major deep network architectures, including Convolutional and R ...