Take a look at our Data Modeling & Design books. Shulph carries a great selection of Data Modeling & Design books, and we are always adding more.
Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book Description In order to build robust deep learning systems, you'll need to understand everything from how neural networks work to training CNN models. In this book, you'll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You'll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you'll focus on variational autoencoders and GANs. You'll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You'll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you'll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you'll understand how to apply deep learning to autonomous vehicles. By the end of this book, you'll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learn Cover advanced and state-of-the-art neural network architectures Understand the theory and math behind neural networks Train DNNs and apply them to modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and variational autoencoders to generate new images Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models Understand DL techniques, such as meta-learning and graph neural networks Who this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
A fast paced guide that will help you learn about Apache Hadoop 3 and its ecosystem Key Features Set up, configure and get started with Hadoop to get useful insights from large data sets Work with the different components of Hadoop such as MapReduce, HDFS and YARN Learn about the new features introduced in Hadoop 3 Book Description Apache Hadoop is a widely used distributed data platform. It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics, including MapReduce, YARN, and HDFS. The book begins with an overview of big data and Apache Hadoop. Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems. The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring. You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark. By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster. What you will learn Store and analyze data at scale using HDFS, MapReduce and YARN Install and configure Hadoop 3 in different modes Use Yarn effectively to run different applications on Hadoop based platform Understand and monitor how Hadoop cluster is managed Consume streaming data using Storm, and then analyze it using Spark Explore Apache Hadoop ecosystem components, such as Flume, Sqoop, HBase, Hive, and Kafka Who this book is for Aspiring Big Data professionals who want to learn the essentials of Hadoop 3 will find this book to be useful. Existing Hadoop users who want to get up to speed with the new features introduced in Hadoop 3 will also benefit from this book. Having knowledge of Java programming will be an added advantage.
Build efficient, high-performance & scalable systems to process large volumes of data with Apache Ignite Key Features Understand Apache Ignite's in-memory technology Create High-Performance app components with Ignite Build a real-time data streaming and complex event processing system Book Description Apache Ignite is a distributed in-memory platform designed to scale and process large volume of data. It can be integrated with microservices as well as monolithic systems, and can be used as a scalable, highly available and performant deployment platform for microservices. This book will teach you to use Apache Ignite for building a high-performance, scalable, highly available system architecture with data integrity. The book takes you through the basics of Apache Ignite and in-memory technologies. You will learn about installation and clustering Ignite nodes, caching topologies, and various caching strategies, such as cache aside, read and write through, and write behind. Next, you will delve into detailed aspects of Ignite's data grid: web session clustering and querying data. You will learn how to process large volumes of data using compute grid and Ignite's map-reduce and executor service. You will learn about the memory architecture of Apache Ignite and monitoring memory and caches. You will use Ignite for complex event processing, event streaming, and the time-series predictions of opportunities and threats. Additionally, you will go through off-heap and on-heap caching, swapping, and native and Spring framework integration with Apache Ignite. By the end of this book, you will be confident with all the features of Apache Ignite 2.x that can be used to build a high-performance system architecture. What you will learn Use Apache Ignite's data grid and implement web session clustering Gain high performance and linear scalability with in-memory distributed data processing Create a microservice on top of Apache Ignite that can scale and perform Perform ACID-compliant CRUD operations on an Ignite cache Retrieve data from Apache Ignite's data grid using SQL, Scan and Lucene Text query Explore complex event processing concepts and event streaming Integrate your Ignite app with the Spring framework Who this book is for The book is for Big Data professionals who want to learn the essentials of Apache Ignite. Prior experience in Java is necessary.
Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key Features Master the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.x and Scala Book Description Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: Mastering Apache Spark 2.x by Romeo Kienzler Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn Get to grips with all the features of Apache Spark 2.x Perform highly optimized real-time big data processing Use ML and DL techniques with Spark MLlib and third-party tools Analyze structured and unstructured data using SparkSQL and GraphX Understand tuning, debugging, and monitoring of big data applications Build scalable and fault-tolerant streaming applications Develop scalable recommendation engines Who this book is for If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the features of Apache Spark and build a strong big data pipeline will also find this Learning Path useful. To grasp the concepts explained in this Learning Path, you must know the fundamentals of Apache Spark and Scala.
Integrate open source data analytics and build business intelligence on SQL databases with Apache Superset. The quick, intuitive nature for data visualization in a web application makes it easy for creating interactive dashboards. Key Features Work with Apache Superset's rich set of data visualizations Create interactive dashboards and data storytelling Easily explore data Book Description Apache Superset is a modern, open source, enterprise-ready business intelligence (BI) web application. With the help of this book, you will see how Superset integrates with popular databases like Postgres, Google BigQuery, Snowflake, and MySQL. You will learn to create real time data visualizations and dashboards on modern web browsers for your organization using Superset. First, we look at the fundamentals of Superset, and then get it up and running. You'll go through the requisite installation, configuration, and deployment. Then, we will discuss different columnar data types, analytics, and the visualizations available. You'll also see the security tools available to the administrator to keep your data safe. You will learn how to visualize relationships as graphs instead of coordinates on plain orthogonal axes. This will help you when you upload your own entity relationship dataset and analyze the dataset in new, different ways. You will also see how to analyze geographical regions by working with location data. Finally, we cover a set of tutorials on dashboard designs frequently used by analysts, business intelligence professionals, and developers. What you will learn Get to grips with the fundamentals of data exploration using Superset Set up a working instance of Superset on cloud services like Google Compute Engine Integrate Superset with SQL databases Build dashboards with Superset Calculate statistics in Superset for numerical, categorical, or text data Understand visualization techniques, filtering, and grouping by aggregation Manage user roles and permissions in Superset Work with SQL Lab Who this book is for This book is for data analysts, BI professionals, and developers who want to learn Apache Superset. If you want to create interactive dashboards from SQL databases, this book is what you need. Working knowledge of Python will be an advantage but not necessary to understand this book.
Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts such as SVM, KNN classifiers, and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learn Get up and running with the Jupyter ecosystem Identify potential areas of investigation and perform exploratory data analysis Plan a machine learning classification strategy and train classification models Use validation curves and dimensionality reduction to tune and enhance your models Scrape tabular data from web pages and transform it into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.
Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ A modern, practical and computational approach to Bayesian statistical modeling A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises. Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. What you will learn Build probabilistic models using the Python library PyMC3 Analyze probabilistic models with the help of ArviZ Acquire the skills required to sanity check models and modify them if necessary Understand the advantages and caveats of hierarchical models Find out how different models can be used to answer different data analysis questions Compare models and choose between alternative ones Discover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian framework Who this book is for If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.
Implement decentralized blockchain applications to build scalable Dapps Key Features Understand the blockchain ecosystem and its terminologies Implement smart contracts, wallets, and consensus protocols Design and develop decentralized applications using Bitcoin, Ethereum, and Hyperledger Book Description The Blockchain is a revolution promising a new world without middlemen. Technically, it is an immutable and tamper-proof distributed ledger of all transactions across a peer-to-peer network. With this book, you will get to grips with the blockchain ecosystem to build real-world projects. This book will walk you through the process of building multiple blockchain projects with different complexity levels and hurdles. Each project will teach you just enough about the field's leading technologies, Bitcoin, Ethereum, Quorum, and Hyperledger in order to be productive from the outset. As you make your way through the chapters, you will cover the major challenges that are associated with blockchain ecosystems such as scalability, integration, and distributed file management. In the concluding chapters, you'll learn to build blockchain projects for business, run your ICO, and even create your own cryptocurrency. Blockchain by Example also covers a range of projects such as Bitcoin payment systems, supply chains on Hyperledger, and developing a Tontine Bank Every is using Ethereum. By the end of this book, you will not only be able to tackle common issues in the blockchain ecosystem, but also design and build reliable and scalable distributed systems. What you will learn Grasp decentralized technology fundamentals to master blockchain principles Build blockchain projects on Bitcoin, Ethereum, and Hyperledger Create your currency and a payment application using Bitcoin Implement decentralized apps and supply chain systems using Hyperledger Write smart contracts, run your ICO, and build a Tontine decentralized app using Ethereum Implement distributed file management with blockchain Integrate blockchain into existing systems in your organization Who this book is for If you are keen on learning how to build your own blockchain decentralized applications from scratch, then this book is for you. It explains all the basic concepts required to develop intermediate projects and will teach you to implement the building blocks of a blockchain ecosystem.
Your one-stop guide to blockchain technology and its business applications Key Features Assimilate blockchain services such as Ethereum and Hyperledger to transform industrial applications Know in and out of blockchain technology to understand various business use cases Understand various common and not-so-common challenges faced in blockchain development Book Description Blockchain for Business 2019 is a comprehensive guide that enables you to bring in various blockchain functionalities to extend your existing business models and make correct fully-informed decisions. You will learn how decentralized applications are transforming numerous business sectors that are expected to play a huge role in the future. You will see how large corporations are already implementing blockchain technology now. You will then learn about the various blockchain services, such as Bitcoin, Ethereum, Hyperledger, and others to understand their use cases in a variety of business domains. You will develop a solid fundamental understanding of blockchain architecture. Moving ahead, you will get to grips with the inner workings of blockchain, with detailed explanations of mining, decentralized consensus, cryptography, smart contracts, and many other important concepts. You will delve into a realistic view of the current state of blockchain technology, along with its issues, limitations, and potential solutions that can take it to the next level. By the end of this book, you will all be well versed in the latest innovations and developments in the emerging blockchain space. What you will learn Understand the fundamentals of blockchain and how it was developed Gain a good understanding of economic concepts and developments Develop a base for concepts such as cryptography, computer networking, and programming Understand the applications of blockchain and its potential impact on the world Become well versed with the latest developments in the blockchain space Explore blockchain frameworks, including decentralized organizational structures, networks, and applications Who this book is for This book is for financial professionals, business executives, managers, and enthusiasts who are interested in getting well-versed with blockchain technology in various business domains. This book will help boost your existing business models using blockchain services. No prior experience of blockchain is required.
Understand how blockchain works and explore a variety of strategies to implement it in your organization effectively Key Features Become familiar with business challenges faced by companies when using blockchain Discover how companies implement blockchain to monetize and secure their data Study real-world examples to understand blockchain and its use in organizations Book Description In addition to cryptocurrencies, blockchain-based apps are being developed in different industries such as banking, supply chain, and healthcare to achieve digital transformation and enhance user experience. Blockchain is not only about Bitcoin or cryptocurrencies, but also about different technologies such as peer-to-peer networks, consensus mechanisms, and cryptography. These technologies together help sustain trustless environments in which digital value can be transferred between individuals without intermediaries. This book will help you understand the basics of blockchain such as consensus protocols, decentralized applications, and tokenization. You'll focus on how blockchain is used today in different industries and the technological challenges faced while implementing a blockchain strategy. The book also enables you, as a decision maker, to understand blockchain from a technical perspective and evaluate its applicability in your business. Finally, you'll get to grips with blockchain frameworks such as Hyperledger and Quorum and their usability. By the end of this book, you'll have learned about the current use cases of blockchain and be able to implement a blockchain strategy on your own. What you will learn Become well-versed with how blockchain works Understand the difference between blockchain and Bitcoin Learn how blockchain is being used in different industry verticals such as finance and retail Delve into the technological and organizational challenges of implementing blockchain Explore the possibilities that blockchain can unlock for decision makers Choose a blockchain framework best suited for your projects from options such as Ethereum and Hyperledger Fabric Who this book is for This book is for CXOs, business professionals, organization leaders, decision makers, technology enthusiasts, and managers who wish to understand how blockchain is implemented in different organizations, its impact, and how it can be customized according to business needs. Prior experience with blockchain is not required.