May 7, 2021

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The Gun Digest Book of Long-Range Shooting

The Gun Digest Book of Long-Range Shooting
Author : Lp Brezny
Publisher : Gun Digest Books
Release Date : 2007-06-24
Category : Sports & Recreation
Total pages :224
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Become a better long-range shot! How well can you shoot at 200 yards? 500? How about 1,000? L.P. Brenzy has taught many shooters the art of accurate, long range rifle work, and in Gun Digest Book of Long-Range Shooting he shares the skills and practice techniques that will put you on target. You learn: How to select the best rifle and caliber to make the shots you want to make How to select the proper bullet and loading information to get the best performance Practice techniques and how to keep the edge needed to routinely make long, accurate shots How to make accurate distance judgments without the use of high dollar electronics How to know your limitations in hunting situations and make ethical shots

Statistical Abstract of the United States 2006: The National Data Book

Statistical Abstract of the United States 2006: The National Data Book
Author : Anonim
Publisher : Government Printing Office
Release Date : 2021
Category : Mathematics
Total pages :129
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Provides the standard summary of statistics on the social, political, and economic organization of the United States. Meant to serve as a convenient volume for statistical reference and as a guide to other statistical publications and sources. In recognition of this being the 125th edition of the "Statistical Abstract", historical tables illustrate the beginning of each section in order to highlight related data when they first appeared in earlier editions.

Introduction to Data Science

Introduction to Data Science
Author : Rafael A. Irizarry
Publisher : CRC Press
Release Date : 2019-11-20
Category : Mathematics
Total pages :713
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Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This 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. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Python Data Science Handbook

Python Data Science Handbook
Author : Jake VanderPlas
Publisher : "O'Reilly Media, Inc."
Release Date : 2016-11-21
Category : Computers
Total pages :548
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For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With 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: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Mining of Massive Datasets

Mining of Massive Datasets
Author : Jure Leskovec,Anand Rajaraman,Jeffrey David Ullman
Publisher : Cambridge University Press
Release Date : 2014-11-13
Category : Computers
Total pages :476
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Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

The Data Warehouse Toolkit

The Data Warehouse Toolkit
Author : Ralph Kimball,Margy Ross
Publisher : John Wiley & Sons
Release Date : 2011-08-08
Category : Computers
Total pages :464
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Evaluating Gun Policy

Evaluating Gun Policy
Author : Jens Ludwig,Philip J. Cook
Publisher : Brookings Institution Press
Release Date : 2004-05-13
Category : Social Science
Total pages :469
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Compared with other developed nations, the United States is unique in its high rates of both gun ownership and murder. Although widespread gun ownership does not have much effect on the overall crime rate, gun use does make criminal violence more lethal and has a unique capacity to terrorize the public. Gun crime accounts for most of the costs of gun violence in the United States, which are on the order of $100 billion per year. But that is not the whole story. Guns also provide recreational benefits and sometimes are used virtuously in fending off or forestalling criminal attacks. Given that guns may be used for both good and ill, the goal of gun policy in the United States has been to reduce the flow of guns to the highest-risk groups while preserving access for most people. There is no lack of opinions on policies to regulate gun commerce, possession, and use, and most policy proposals spark intense controversy. Whether the current system achieves the proper balance between preserving access and preventing misuse remains the subject of considerable debate. Evaluating Gun Policy provides guidance for a pragmatic approach to gun policy using good empirical research to help resolve conflicting assertions about the effects of guns, gun control, and law enforcement. The chapters in this volume do not conform neatly to the claims of any one political position. The book is divided into five parts. In the first section, contributors analyze the connections between rates of gun ownership and two outcomes of particular interest to society—suicide and burglary. Regulating ownership is the focus of the second section, where contributors investigate the consequences a large-scale combined gun ban and buy-back program in Australia, as well as the impact of state laws that prohibit gun ownership to those with histories of domestic violence. The third section focuses on efforts to restrict gun carrying and includes a critical examination of efforts in Pittsburgh to patrol illegal gun traffic and a re-examination of the effects of permissive state gun-carrying laws. This section also features the first rigorous—and critical—analysis of Richmond's Project Exile, which serves as one model for the national Project Safe Neighborhoods program. The fourth section focuses on efforts to facilitate research on gun violence, including a database on state gun laws and the ongoing development of a nationwide violent-death reporting system. The book concludes with an examination of the policy process. Differences in opinion about gun policy flourish partly because of the lack of sound evidence in this area. The contributors to this volume demonstrate that skilled and dispassionate analysis of the evidence is attainable, even in an area as contentious as firearm policy. For pragmatists who wish to reduce the social burden of gun violence, there is no acceptable alternative.

Ballistic Preformance of Rifle Bullets

Ballistic Preformance of Rifle Bullets
Author : Bryan Litz
Publisher : Unknown
Release Date : 2014-11-01
Category :
Total pages :481
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State and Metropolitan Area Data

State and Metropolitan Area Data
Author : Deirdre A. Gaquin,Mary Meghan Ryan
Publisher : Bernan Press
Release Date : 2017-09-28
Category : Business & Economics
Total pages :478
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The State and Metropolitan Area Data Book is the continuation of the U.S. Census Bureau’s discontinued publication. It is a convenient summary of statistics on the social and economic structure of the states, metropolitan areas, and micropolitan areas in the United States. It is designed to serve as a statistical reference and guide to other data publications and sources. This new edition features more than 1,500 data items from a variety of sources. It covers many key topical areas including population, birth and death rates, health coverage, school enrollment, crime rates, income and housing, employment, transportation, and government. The metropolitan area information is based on the latest set of definitions of metropolitan and micropolitan areas including: a complete listing and data for all states, metropolitan areas, including micropolitan areas, and their component counties 2010 census counts and more recent population estimates for all areas results of the 2016 national and state elections expanded vital statistics, communication, and criminal justice data data on migration and commuting habits American Community Survey 1- and 3-year estimates data on health insurance and housing and finance matters accurate and helpful citations to allow the user to directly consult the source source notes and explanations A guide to state statistical abstracts and state information Economic development officials, regional planners, urban researchers, college students, and data users can easily see the trends and changes affecting the nation today.

Rifle Marksmanship

Rifle Marksmanship
Author : U. S. Marine Corps
Publisher : Createspace Independent Publishing Platform
Release Date : 2018-02-23
Category :
Total pages :250
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Rifle Marksmanship, the official U.S. Marines reference publication 3-01A, is the complete guide to becoming proficient on the Corps' service rifles: the M16A4 and the M4 carbine. In addition to marksmanship techniques, the manual covers component parts, ammunition; cleaning, servicing and maintenance; operation and safety; firing positions; and engagement and low-light techniques. Illustrated throughout with clear and detailed photographs and drawings, Rifle Marksmanship will be of value to anyone interested in becoming a better rifle marksman, regardless of gun fired. From the Foreword: Marine Corps Reference Publication 3-01A, Rifle Marksmanship, presents how the Service rifle/carbine is employed by the individual Marine, contains the skills and techniques required for individual rifle marksmanship, and provides the skills that are required for basic through advanced marksmanship. This publication is intended to be used as a reference guide concerning marksmanship skills for unit commanders, trainers, and individual Marines. It presents marksmanship techniques organized by topic, and within each of those topics, techniques are broken down further as they apply to employment with the rifle combat optic (the primary sighting system) and with backup iron sights.

Web Scraping with Python

Web Scraping with Python
Author : Ryan Mitchell
Publisher : "O'Reilly Media, Inc."
Release Date : 2015-06-15
Category : Computers
Total pages :256
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Learn web scraping and crawling techniques to access unlimited data from any web source in any format. With this practical guide, you’ll learn how to use Python scripts and web APIs to gather and process data from thousands—or even millions—of web pages at once. Ideal for programmers, security professionals, and web administrators familiar with Python, this book not only teaches basic web scraping mechanics, but also delves into more advanced topics, such as analyzing raw data or using scrapers for frontend website testing. Code samples are available to help you understand the concepts in practice. Learn how to parse complicated HTML pages Traverse multiple pages and sites Get a general overview of APIs and how they work Learn several methods for storing the data you scrape Download, read, and extract data from documents Use tools and techniques to clean badly formatted data Read and write natural languages Crawl through forms and logins Understand how to scrape JavaScript Learn image processing and text recognition

Fighting Churn with Data

Fighting Churn with Data
Author : Carl S. Gold
Publisher : Manning Publications
Release Date : 2020-12-22
Category : Computers
Total pages :504
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The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. Summary The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. This hands-on guide is packed with techniques for converting raw data into measurable metrics, testing hypotheses, and presenting findings that are easily understandable to non-technical decision makers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Keeping customers active and engaged is essential for any business that relies on recurring revenue and repeat sales. Customer turnover—or “churn”—is costly, frustrating, and preventable. By applying the techniques in this book, you can identify the warning signs of churn and learn to catch customers before they leave. About the book Fighting Churn with Data teaches developers and data scientists proven techniques for stopping churn before it happens. Packed with real-world use cases and examples, this book teaches you to convert raw data into measurable behavior metrics, calculate customer lifetime value, and improve churn forecasting with demographic data. By following Zuora Chief Data Scientist Carl Gold’s methods, you’ll reap the benefits of high customer retention. What's inside Calculating churn metrics Identifying user behavior that predicts churn Using churn reduction tactics with customer segmentation Applying churn analysis techniques to other business areas Using AI for accurate churn forecasting About the reader For readers with basic data analysis skills, including Python and SQL. About the author Carl Gold (PhD) is the Chief Data Scientist at Zuora, Inc., the industry-leading subscription management platform. Table of Contents: PART 1 - BUILDING YOUR ARSENAL 1 The world of churn 2 Measuring churn 3 Measuring customers 4 Observing renewal and churn PART 2 - WAGING THE WAR 5 Understanding churn and behavior with metrics 6 Relationships between customer behaviors 7 Segmenting customers with advanced metrics PART 3 - SPECIAL WEAPONS AND TACTICS 8 Forecasting churn 9 Forecast accuracy and machine learning 10 Churn demographics and firmographics 11 Leading the fight against churn

Deep Learning with PyTorch

Deep Learning with PyTorch
Author : Eli Stevens,Luca Antiga,Thomas Viehmann
Publisher : Manning Publications
Release Date : 2020-08-04
Category : Computers
Total pages :520
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Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Summary Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Foreword by Soumith Chintala, Cocreator of PyTorch. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Although many deep learning tools use Python, the PyTorch library is truly Pythonic. Instantly familiar to anyone who knows PyData tools like NumPy and scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s excellent for building quick models, and it scales smoothly from laptop to enterprise. Because companies like Apple, Facebook, and JPMorgan Chase rely on PyTorch, it’s a great skill to have as you expand your career options. It’s easy to get started with PyTorch. It minimizes cognitive overhead without sacrificing the access to advanced features, meaning you can focus on what matters the most - building and training the latest and greatest deep learning models and contribute to making a dent in the world. PyTorch is also a snap to scale and extend, and it partners well with other Python tooling. PyTorch has been adopted by hundreds of deep learning practitioners and several first-class players like FAIR, OpenAI, FastAI and Purdue. About the book Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning. What's inside Training deep neural networks Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Exploring code samples in Jupyter Notebooks About the reader For Python programmers with an interest in machine learning. About the author Eli Stevens had roles from software engineer to CTO, and is currently working on machine learning in the self-driving-car industry. Luca Antiga is cofounder of an AI engineering company and an AI tech startup, as well as a former PyTorch contributor. Thomas Viehmann is a PyTorch core developer and machine learning trainer and consultant. consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

Introduction to Information Retrieval

Introduction to Information Retrieval
Author : Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze
Publisher : Cambridge University Press
Release Date : 2008-07-07
Category : Computers
Total pages :129
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Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

Ecological Models and Data in R

Ecological Models and Data in R
Author : Benjamin M. Bolker
Publisher : Princeton University Press
Release Date : 2008-07-21
Category : Computers
Total pages :396
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Introduction and background; Exploratory data analysis and graphics; Deterministic functions for ecological modeling; Probability and stochastic distributions for ecological modeling; Stochatsic simulation and power analysis; Likelihood and all that; Optimization and all that; Likelihood examples; Standar statistics revisited; Modeling variance; Dynamic models.