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. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you’ll examine how to analyze data at scale to derive insights from large datasets efficiently. 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 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 ... 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 ... This book is assembled from lectures given by the author over a period of 10 years at the School of Computing of DePaul University. 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 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 ... Presents case studies and instructions on how to solve data analysis problems using Python. 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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. 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 ... Found insideThis volume offers an overview of current efforts to deal with dataset and covariate shift. 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 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 insidePython Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Found inside – Page iPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. This text draws on that experience, as well as on computer vision courses he has taught at the University of Washington and Stanford. Found insideEvery chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. 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. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Found insideThis practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. 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 ... Found insideWith Algorithms for Python, Fourth Edition Morton John Canty. Bridle, J. S. (1990). ... Mean shift: A robust approach toward feature space analysis. 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 ... A far-reaching course in practical advanced statistics for biologists using R/Bioconductor, data exploration, and simulation. 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. Operators are a way of packaging, deploying, and managing Kubernetes applications. 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 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 ... 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 ... Found inside – Page 208We will try out the different clustering algorithms available in ... MeanShift, MiniBatchKMeans, AgglomerativeClustering, SpectralClustering) julia> ... 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 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 530A Practical Python Guide for the Analysis of Survey Data, Updated Edition ... 266 max-radius minimization, 264 mean shift, 265 minimum spanning tree, ... Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists. 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. 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. Enhances Python skills by working with data structures and algorithms and gives examples of complex systems using exercises, case studies, and simple explanations. 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. 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 ... 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