Found insideThis edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. Found insideThis book constitutes the refereed proceedings of 3 workshops co-located with International Conference for High Performance Computing, Networking, Storage, and Analysis, SC19, held in Denver, CO, USA, in November 2019. Found insideThis book is a printed edition of the Special Issue "Air Quality Monitoring and Forecasting" that was published in Atmosphere Found insideWritten by three leading researchers in the field, this book provides in-depth coverage of the theory concerning the logical level of database management systems, including both classical and advanced topics. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Found insideThe book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Found inside – Page 125Dask supports both NumPy array and pandas DataFrame data structures, ... Later, you'll learn how parallelism in Dask works with dask.delayed. Found insideWith this Learning Path, you will gain complete knowledge to solve problems by building high performing applications loaded with asynchronous, multithreaded code and proven design patterns. Found inside – Page iThis book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Found insideIn this book, we will use different complexities of datasets in order to build end-to-end projects. Found insideIf you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Found insideEstimation of Rare Event Probabilities in Complex Aerospace and Other Systems: A Practical Approach provides a broad up-to-date view of the current available techniques to estimate rare event probabilities described with a unified notation, ... You’ll explore: Data scientist skills—and why every company needs a Spock How the benefits of giving company-wide access to data outweigh the costs Why data-driven organizations use the scientific method to explore and solve data ... Found insideHis new book, Excellence Wins, weaves Horst's personal story with the practical wisdom he's gleaned from an incredible career of servant leadership. This volume details computational techniques for analyses of a wide range of biological contexts, providing an overview of the most up-to-date techniques used in the field. Found insideDistributed DataFrame dataframe enables distributed and larger-than-RAM Pandas ... Delayed delayed extends the idea we introduced with Joblib in “Replacing ... These are then brought together by implementing deep reinforcement learning for automated trading. This book will serve as a continuing reference for implementing deep learning models to build investment strategies. This edition contains a large number of additions and corrections scattered throughout the text, including the incorporation of a new chapter on state-space models. This guide for software architects builds upon legacies of best practice, explaining key areas and how to make architectural designs successful. This updated edition describes both the mathematical theory behind a modern photorealistic rendering system as well as its practical implementation. Found insideProbability is the bedrock of machine learning. This book is the only title that addresses the nuts-and-bolts issues involved in design, installation and troubleshooting that are the day-to-day concern of engineers and network specialists working in industry. * Provides a unique focus on ... The total of 65 full papers and 168 workshop papers presented in this book set were carefully reviewed and selected from 573 submissions (228 submissions to the main track and 345 submissions to the workshops). Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists. Found insideThis book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Big Data Benchmarking, WBDB 2014, held in Potsdam, Germany, in August 2014. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. Found insideUsing clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, ... This book is relevant to any kind of business and is currently being used by a number of multi-national companies, including AstraZeneca, Ericsson, Scania and Volvo. Found inside – Page iiThis book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. Key Features This is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book Description The ... You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Found insideThis book has everything you need to gain a firm grip on how high-frequency trading works and what it takes to apply it to your everyday trading endeavors. This book is a definite must have for any budding iPhone developer." –Peter Watling, New Zealand, Developer of BubbleWrap Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. The escalating demand for ubiquitous computing along with the complementary and flexible natures of Radio Frequency Identification (RFID) and Wireless Sensor Networks (WSNs) have sparked an increase in the integration of these two dynamic ... While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. This is exactly the topic of this book. This book constitutes the proceedings of the 25th International Conference on Parallel and Distributed Computing, Euro-Par 2019, held in Göttingen, Germany, in August 2019. Found insideThis practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats—by using Python. Presents case studies and instructions on how to solve data analysis problems using Python. Found inside – Page iThis book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. In a market that is crowded with high-level monographs and reference guides, more practical information for professional engineers is required. This book gives them the knowledge to design their next SCADA system more effectively. You are required to have a basic knowledge of Python development to get the most of this book. Found insideOver 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data ... Found inside – Page 27Let's look at some of these cases: • Requesting a sample: For example, ... Note that if we call compute directly on a Dask DataFrame instead, ... Found inside – Page 421Storing a Dask Bag in a DataFrame Let's store a Dask Bag in a DataFrame: ... in the following output: In the preceding example, we created a Dask Bag ... Found insideWith this book, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach. 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