Take a look at our Data Processing books. Shulph carries a great selection of Data Processing books, and we are always adding more.
Understand the Ethereum platform to build distributed applications that are secured and decentralized using blockchain technology Key Features Build your own decentralized applications using real-world blockchain examples Implement Ethereum for building smart contracts and cryptocurrency applications with easy-to-follow projects Enhance your application security with blockchain Book Description Ethereum enables the development of efficient, smart contracts that contain code. These smart contracts can interact with other smart contracts to make decisions, store data, and send Ether to others.Ethereum Projects for Beginners provides you with a clear introduction to creating cryptocurrencies, smart contracts, and decentralized applications. As you make your way through the book, you'll get to grips with detailed step-by-step processes to build advanced Ethereum projects. Each project will teach you enough about Ethereum to be productive right away. You will learn how tokenization works, think in a decentralized way, and build blockchain-based distributed computing systems. Towards the end of the book, you will develop interesting Ethereum projects such as creating wallets and secure data sharing.By the end of this book, you will be able to tackle blockchain challenges by implementing end-to-end projects using the full power of the Ethereum blockchain. What you will learn Develop your ideas fast and efficiently using the Ethereum blockchain Make writing and deploying smart contracts easy and manageable Work with private data in blockchain applications Handle large files in blockchain applications Ensure your decentralized applications are safe Explore how Ethereum development frameworks work Create your own cryptocurrency or token on the Ethereum blockchain Make sure your cryptocurrency is ERC20-compliant to launch an ICO Who this book is for This book is for individuals who want to build decentralized applications using blockchain technology and the power of Ethereum from scratch. Some prior knowledge of JavaScript is required, since most examples use a web frontend.
Explore what functional programming is, how it is used, and the features of Python that support it Key Features Study in detail all aspects of functional programming, including immutability, generators, and more Reinforce your learning through elaborate examples Learn how to implement advanced topics like closures, memoization, and monads in your applications Book Description Python supports four programming paradigms – imperative, procedural, object-oriented, and functional. Of these, functional programming is probably the least understood and the least used. This book covers several topics that are directly and indirectly related to functional programming. After a quick overview of functional programming and its characteristics, Functional Programming in Python explains the various concepts of Python, starting with functions. You’ll learn how to change the value of an object by using mutability. You'll then look at recursion as a more functional alternative to looping for certain algorithms, and learn how memoization alleviates the limitations of recursion in certain situations. The book further explains how to use closures as function factories and how to handle errors and exceptions with functors and monads. By the end of this book, you’ll have all the knowledge you need for developing your applications with functional programming in Python. What you will learn Understand the advantages and disadvantages of functional programming Use closures in your code to dynamically create functions Create your own iterators Use the general-purpose functools to create your own specialized reducing functions Study and implement list and generator comprehensions to create lists Create customized iterators with generators Who this book is for If you are a developer looking to create applications in Python using functional programming, this book is ideal for you. You will only need a basic knowledge of Python. Prior knowledge or experience of functional programming is not required.
Leverage the power of Tableau to get actionable business insights and make better business decisions Key Features Explore all the new features of Tableau 2018.x Begin your Tableau journey by understanding its core functionalities Apply advanced techniques such as data blending and parameters to make the best of Tableau Book Description Tableau is one of the leading business intelligence tools used worldwide, in organizations of every scale. In its latest release, Tableau 2018 promises richer and more useful features related to visual analytics, reporting, dashboarding, and a host of other data visualization aspects. Getting Started with Tableau 2018.x will get you up and running with these features. The book starts with all the new functionalities of the different Tableau 2018 versions, along with concrete examples of how to use them. However, if you're new to Tableau, don't worry! The rest of the book will guide you through each major aspect of Tableau with examples. You'll learn how to connect to data, build a data source, visualize your data, build a dashboard, and share it online. In the final chapters, you'll also learn advanced techniques such as creating a cross-database join, data blending, and more. By the end of the book, you will have a firm understanding of how to effectively use Tableau to create quick, cost-effective, and business-efficient business intelligence solutions. What you will learn Discover new functionalities such as density, extensions, and transparency introduced in Tableau 2018.x Connect tables and make transformations such as pivoting the field and splitting columns Build an efficient data source for your analysis Design insightful data visualization using different mark types and properties Build powerful dashboards and stories Share your work and interact with Tableau Server Use Tableau to explore your data and find new insights Explore Tableau's advanced features and gear up for upcoming challenges Who this book is for If you're a beginner or an existing BI professional looking to get the most out of Tableau 2018's latest features, this book is for you. You'll also find this book useful if you're an aspiring analyst who wants to learn the capabilities of Tableau to answer business-specific questions. No prior knowledge or experience with Tableau or any other Business Intelligence tool is necessary.
Build smarter systems by combining artificial intelligence and the Internet of Things—two of the most talked about topics today Key Features Leverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT data Process IoT data and predict outcomes in real time to build smart IoT models Cover practical case studies on industrial IoT, smart cities, and home automation Book Description There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence. What you will learn Apply different AI techniques including machine learning and deep learning using TensorFlow and Keras Access and process data from various distributed sources Perform supervised and unsupervised machine learning for IoT data Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms Forecast time-series data using deep learning methods Implementing AI from case studies in Personal IoT, Industrial IoT, and Smart Cities Gain unique insights from data obtained from wearable devices and smart devices Who this book is for If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book.
Implement real-world decentralized applications using Python, Vyper, Populus, and Ethereum Key Features Stay up-to-date with everything you need to know about the blockchain ecosystem Implement smart contracts, wallets, and decentralized applications(DApps) using Python libraries Get deeper insights into storing content in a distributed storage platform Book Description Blockchain is seen as the main technological solution that works as a public ledger for all cryptocurrency transactions. This book serves as a practical guide to developing a full-fledged decentralized application with Python to interact with the various building blocks of blockchain applications. Hands-On Blockchain for Python Developers starts by demonstrating how blockchain technology and cryptocurrency hashing works. You will understand the fundamentals and benefits of smart contracts such as censorship resistance and transaction accuracy. As you steadily progress, you'll go on to build smart contracts using Vyper, which has a similar syntax to Python. This experience will further help you unravel the other benefits of smart contracts, including reliable storage and backup, and efficiency. You'll also use web3.py to interact with smart contracts and leverage the power of both the web3.py and Populus framework to build decentralized applications that offer security and seamless integration with cryptocurrencies. As you explore later chapters, you'll learn how to create your own token on top of Ethereum and build a cryptocurrency wallet graphical user interface (GUI) that can handle Ethereum and Ethereum Request for Comments (ERC-20) tokens using the PySide2 library. This will enable users to seamlessly store, send, and receive digital money. Toward the end, you'll implement InterPlanetary File System (IPFS) technology in your decentralized application to provide a peer-to-peer filesystem that can store and expose media. By the end of this book, you'll be well-versed in blockchain programming and be able to build end-to-end decentralized applications on a range of domains using Python. What you will learn Understand blockchain technology and what makes it an immutable database Use the features of web3.py API to interact with the smart contract Create your own cryptocurrency and token in Ethereum using Vyper Use IPFS features to store content on the decentralized storage platform Implement a Twitter-like decentralized application with a desktop frontend Build decentralized applications in the shape of console, web, and desktop applications Who this book is for If you are a Python developer who wants to enter the world of blockchain, Hands-On Blockchain for Python Developers is for you. The book will be your go-to guide to becoming well-versed with the blockchain ecosystem and building your own decentralized applications using Python and library support.
Speed up the design and implementation of deep learning solutions using Apache Spark Key Features Explore the world of distributed deep learning with Apache Spark Train neural networks with deep learning libraries such as BigDL and TensorFlow Develop Spark deep learning applications to intelligently handle large and complex datasets Book Description Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases. What you will learn Understand the basics of deep learning Set up Apache Spark for deep learning Understand the principles of distribution modeling and different types of neural networks Obtain an understanding of deep learning algorithms Discover textual analysis and deep learning with Spark Use popular deep learning frameworks, such as Deeplearning4j, TensorFlow, and Keras Explore popular deep learning algorithms Who this book is for If you are a Scala developer, data scientist, or data analyst who wants to learn how to use Spark for implementing efficient deep learning models, Hands-On Deep Learning with Apache Spark is for you. Knowledge of the core machine learning concepts and some exposure to Spark will be helpful.
Practical examples with real-world projects in GIS, Remote sensing, Geospatial data management and Analysis using the R programming language Key Features Understand the basics of R and QGIS to work with GIS and remote sensing data Learn to manage, manipulate, and analyze spatial data using R and QGIS Apply machine learning algorithms to geospatial data using R and QGIS Book Description Managing spatial data has always been challenging and it's getting more complex as the size of data increases. Spatial data is actually big data and you need different tools and techniques to work your way around to model and create different workflows. R and QGIS have powerful features that can make this job easier. This book is your companion for applying machine learning algorithms on GIS and remote sensing data. You'll start by gaining an understanding of the nature of spatial data and installing R and QGIS. Then, you'll learn how to use different R packages to import, export, and visualize data, before doing the same in QGIS. Screenshots are included to ease your understanding. Moving on, you'll learn about different aspects of managing and analyzing spatial data, before diving into advanced topics. You'll create powerful data visualizations using ggplot2, ggmap, raster, and other packages of R. You'll learn how to use QGIS 3.2.2 to visualize and manage (create, edit, and format) spatial data. Different types of spatial analysis are also covered using R. Finally, you'll work with landslide data from Bangladesh to create a landslide susceptibility map using different machine learning algorithms. By reading this book, you'll transition from being a beginner to an intermediate user of GIS and remote sensing data in no time. What you will learn Install R and QGIS Get familiar with the basics of R programming and QGIS Visualize quantitative and qualitative data to create maps Find out the basics of raster data and how to use them in R and QGIS Perform geoprocessing tasks and automate them using the graphical modeler of QGIS Apply different machine learning algorithms on satellite data for landslide susceptibility mapping and prediction Who this book is for This book is great for geographers, environmental scientists, statisticians, and every professional who deals with spatial data. If you want to learn how to handle GIS and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful but is not necessary.
Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement learning models Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn Integrate machine learning models into a live trading strategy on Quantopian Evaluate strategies using reliable backtesting methodologies for time series Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow Work with reinforcement learning for trading strategies in the OpenAI Gym Who this book is for Hands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.
With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is for If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.
Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI Gym Key Features Explore the design principles of reinforcement learning and deep reinforcement learning models Use dynamic programming to solve design issues related to building a self-learning system Learn how to systematically implement reinforcement learning algorithms Book Description Reinforcement learning (RL) is an integral part of machine learning (ML), and is used to train algorithms. With this book, you'll learn how to implement reinforcement learning with R, exploring practical examples such as using tabular Q-learning to control robots. You'll begin by learning the basic RL concepts, covering the agent-environment interface, Markov Decision Processes (MDPs), and policy gradient methods. You'll then use R's libraries to develop a model based on Markov chains. You will also learn how to solve a multi-armed bandit problem using various R packages. By applying dynamic programming and Monte Carlo methods, you will also find the best policy to make predictions. As you progress, you'll use Temporal Difference (TD) learning for vehicle routing problem applications. Gradually, you'll apply the concepts you've learned to real-world problems, including fraud detection in finance, and TD learning for planning activities in the healthcare sector. You'll explore deep reinforcement learning using Keras, which uses the power of neural networks to increase RL's potential. Finally, you'll discover the scope of RL and explore the challenges in building and deploying machine learning models. By the end of this book, you'll be well-versed with RL and have the skills you need to efficiently implement it with R. What you will learn Understand how to use MDP to manage complex scenarios Solve classic reinforcement learning problems such as the multi-armed bandit model Use dynamic programming for optimal policy searching Adopt Monte Carlo methods for prediction Apply TD learning to search for the best path Use tabular Q-learning to control robots Handle environments using the OpenAI library to simulate real-world applications Develop deep Q-learning algorithms to improve model performance Who this book is for This book is for anyone who wants to learn about reinforcement learning with R from scratch. A solid understanding of R and basic knowledge of machine learning are necessary to grasp the topics covered in the book.