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Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. Key Features Leverage the Jupyter Notebook for;
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computing. It is ideal for users who wish to learn about the interactive and parallel computing properties of IPython 4.0, along with its;
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Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists;
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, visualization, and data analysis, this is the book for you. This book will prove valuable for anyone that needs to do computations in an agile;
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Get started with Python for data analysis and numerical computing in the Jupyter notebook About This Book * Learn the basics;
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Manipulate plotting parameters and styles to create appealing plots Customize data visualization for different audiences Design data visualizations;
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plotting library matplotlib for data visualization * Data analysis and visualization using Pandas, matplotlib, and IPython * Performing parallel;
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machine learning algorithm) and generating software visualizations from GitHub. This book covers aspects that are relevant for engineers;
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do you get them all-IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar;
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Interactive display and visualization of large geometric and textured models is becoming a fundamental capability. There are numerous;
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raw data, and then transform that data to produce new images of the world. Beginning Python Visualization: Crafting Visual Transformation;
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spectrum of data visualization and its importance. Designed for beginners, it'll help you learn about statistics by computing mean, median, and;
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through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop (R), Spark (TM) and;
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available. We will use it for interactive computing, data analysis, and visualization. In the following chapters, we will explore using Scala's;
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: prediction; data mining; information retrieval; game playing; robotics; learning methods; pattern visualization; automated knowledge acquisition;
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create e ffective visualizations for mobile devices? This book introduces key concepts of mobile data visualization and discusses opportunities;
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Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale, International Workshop on OpenPOWER for HPC (IWOPH18), IXPUG;
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-accelerated graphics interface for visualizing large 2D and 3D datasets. Data visualization has become increasingly challenging using conventional;
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Prevalent types of data in scientific visualization are volumetric data, vector field data, and particle-based data. Particle data;
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map data to a visual framework * Recognize which visualization method is applicable and learn best practices for data visualization * Get;
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of data handling, preprocessing, and visualization. The book concludes with machine learning algorithms and their applications in biological data;
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of data handling, preprocessing, and visualization. The book concludes with machine learning algorithms and their applications in biological data;
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researched. Graphical Thinking for Science and Technology Through Knowledge Visualization provides emerging research exploring the theoretical and;
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researched. Graphical Thinking for Science and Technology Through Knowledge Visualization provides emerging research exploring the theoretical and;
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reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal;
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reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal;
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organized in topical sections named: learning games and visualization; virtual reality and applications; 3D graphics technology, multimedia;
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