This book examines and explains a variety of scientific programming models (programming models relevant to scientists) with an emphasis on how programming constructs map to different parts of the computer's architecture. Found insideMDRM can be thought of as clustering on a manifold when all the labels are known. ... can be found in Python at https://github.com/alexandrebarachant. Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists. This text draws on that experience, as well as on computer vision courses he has taught at the University of Washington and Stanford. Explore the web and make smarter predictions using Python About This Book Targets two big and prominent markets where sophisticated web apps are of need and importance. Found insideWith this practical guide, you'll learn how to conduct analytics on data where it lives, whether it's Hive, Cassandra, a relational database, or a proprietary data store. Found inside – Page iThis book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response ... Found insideBecome an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data ... A far-reaching course in practical advanced statistics for biologists using R/Bioconductor, data exploration, and simulation. Found insideStarting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business ... Found insideIn this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. Found inside – Page xiiPython is a general-purpose pro‐gramming language that has found a home ... We hope that this will mean a more concise and accessible read for a more ... Found insideWith this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas ... The book then discusses SSL applications and offers guidelines for SSLpractitioners by analyzing the results of extensive benchmark experiments. Finally, the book looksat interesting directions for SSL research. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine ... Operators are a way of packaging, deploying, and managing Kubernetes applications. Found inside – Page 1This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. Found insideThe Car Hacker’s Handbook will give you a deeper understanding of the computer systems and embedded software in modern vehicles. Found insidePython Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Found inside – Page 1With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data ... Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... Found insideThis book is about making machine learning models and their decisions interpretable. Found insideBuild application container images from source and deploy them Implement and extend application image builders Use incremental and chained builds to accelerate build times Automate builds by using a webhook to link OpenShift to a Git ... Found inside – Page 208We will try out the different clustering algorithms available in ... MeanShift, MiniBatchKMeans, AgglomerativeClustering, SpectralClustering) julia> ... Found insideOver 60 practical recipes on data exploration and analysis About This Book Clean dirty data, extract accurate information, and explore the relationships between variables Forecast the output of an electric plant and the water flow of ... This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. Found insideEvery chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. Found inside – Page iMany of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. Enhances Python skills by working with data structures and algorithms and gives examples of complex systems using exercises, case studies, and simple explanations. Found insideSolve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of ... Found inside – Page iPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Found inside – Page iBridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. 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. This book is assembled from lectures given by the author over a period of 10 years at the School of Computing of DePaul University. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and ... This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. For many organizations, a big part of DevOps’ appeal is software automation using infrastructure-as-code techniques. This book presents developers, architects, and infra-ops engineers with a more practical option. The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Found inside – Page 530A Practical Python Guide for the Analysis of Survey Data, Updated Edition ... 266 max-radius minimization, 264 mean shift, 265 minimum spanning tree, ... Found insideThis volume offers an overview of current efforts to deal with dataset and covariate shift. Found inside – Page 612Srinivasan, M., Moon, Y.B.: A comprehensive clustering algorithm for strategic ... 30(4), 891–927 (2016) Comaniciu, D., Meer, P.: Mean shift: a robust ... In four parts, this book includes: Getting Started: Jump into Python, the command line, data containers, functions, flow control and logic, and classes and objects Getting It Done: Learn about regular expressions, analysis and visualization ... Found inside – Page 192Bogdan Georgescu and Chris M. Christoudias developed the mean shift algorithm and implemented it in C++. The Python implementation of the same algorithm is ... Found inside – Page 1About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Found insideWith Algorithms for Python, Fourth Edition Morton John Canty. Bridle, J. S. (1990). ... Mean shift: A robust approach toward feature space analysis. Found insideThis practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. 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