How to Model it

How to Model it PDF Author: A. M. Starfield
Publisher: Burgess International Group Incorporated
ISBN:
Category : Language Arts & Disciplines
Languages : en
Pages : 232

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Book Description

How to Model it

How to Model it PDF Author: A. M. Starfield
Publisher: Burgess International Group Incorporated
ISBN:
Category : Language Arts & Disciplines
Languages : en
Pages : 232

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Book Description


Model-Based Machine Learning

Model-Based Machine Learning PDF Author: John Winn
Publisher: CRC Press
ISBN: 1498756824
Category : Business & Economics
Languages : en
Pages : 469

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Book Description
Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features: Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. Explains machine learning concepts as they arise in real-world case studies. Shows how to diagnose, understand and address problems with machine learning systems. Full source code available, allowing models and results to be reproduced and explored. Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

Discrete Choice Methods with Simulation

Discrete Choice Methods with Simulation PDF Author: Kenneth Train
Publisher: Cambridge University Press
ISBN: 0521766559
Category : Business & Economics
Languages : en
Pages : 399

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Book Description
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

R for Data Science

R for Data Science PDF Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521

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Book Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

How to Become an It Architect

How to Become an It Architect PDF Author: Cristian Bojinca
Publisher: Artech House
ISBN: 1630814342
Category : Computers
Languages : en
Pages : 295

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Book Description
Defining the various types of IT architecture in the industry, this one-of-a-kind resource highlights the rewards of becoming an architect and explores the details of the deliverables, project structure, and how to approach their creation. --

Interpretable Machine Learning

Interpretable Machine Learning PDF Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Artificial intelligence
Languages : en
Pages : 320

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Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

How to Be a Model

How to Be a Model PDF Author: Alexandre Ahlberg
Publisher: Createspace Independent Pub
ISBN: 9781500712488
Category : Performing Arts
Languages : en
Pages : 68

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Book Description
How to Be a Model - The Ultimate Guide to Become a Model (The step-by-step guide to getting started as a professional Fashion Model) With awesomely fun references to pop culture and inspiring, memorable quotes, this book will pick up anyone's confidence from the floor and skyrocket it so they can make their dreams of modeling come true! Drive The Agents and Scouts Wild is a fun, quick step-by-step read on how to build your confidence, wipe out all negative energy, and take care of yourself so you can go from dreaming about being a model to owning the catwalk and seeing your own face on magazine covers. Enjoy a fun read while learning how to rise above the naysayers and prove to anyone out there that going from working at a fast food restaurant to living the glamorous life of a model is possible. You can do it, and this book is a perfect how-to.

How to Become a Successful Commercial Model

How to Become a Successful Commercial Model PDF Author: Aaron Marcus
Publisher:
ISBN: 9780965358514
Category : Business & Economics
Languages : en
Pages : 114

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Book Description
Enables you to: get started; find agents and photographers; practice in front of a still camera; create powerful head shots and composites; market yourself and get work on your own; manage and understand the business of modelling; and impress agents, photographers and art directors and get them to cast you again and again.

Principles of Model Checking

Principles of Model Checking PDF Author: Christel Baier
Publisher: MIT Press
ISBN: 0262304031
Category : Computers
Languages : en
Pages : 994

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Book Description
A comprehensive introduction to the foundations of model checking, a fully automated technique for finding flaws in hardware and software; with extensive examples and both practical and theoretical exercises. Our growing dependence on increasingly complex computer and software systems necessitates the development of formalisms, techniques, and tools for assessing functional properties of these systems. One such technique that has emerged in the last twenty years is model checking, which systematically (and automatically) checks whether a model of a given system satisfies a desired property such as deadlock freedom, invariants, and request-response properties. This automated technique for verification and debugging has developed into a mature and widely used approach with many applications. Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field. The book begins with the basic principles for modeling concurrent and communicating systems, introduces different classes of properties (including safety and liveness), presents the notion of fairness, and provides automata-based algorithms for these properties. It introduces the temporal logics LTL and CTL, compares them, and covers algorithms for verifying these logics, discussing real-time systems as well as systems subject to random phenomena. Separate chapters treat such efficiency-improving techniques as abstraction and symbolic manipulation. The book includes an extensive set of examples (most of which run through several chapters) and a complete set of basic results accompanied by detailed proofs. Each chapter concludes with a summary, bibliographic notes, and an extensive list of exercises of both practical and theoretical nature.

Model Behavior

Model Behavior PDF Author: Nicole C. Nelson
Publisher: University of Chicago Press
ISBN: 022654611X
Category : Science
Languages : en
Pages : 262

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Book Description
Mice are used as model organisms across a wide range of fields in science today—but it is far from obvious how studying a mouse in a maze can help us understand human problems like alcoholism or anxiety. How do scientists convince funders, fellow scientists, the general public, and even themselves that animal experiments are a good way of producing knowledge about the genetics of human behavior? In Model Behavior, Nicole C. Nelson takes us inside an animal behavior genetics laboratory to examine how scientists create and manage the foundational knowledge of their field. Behavior genetics is a particularly challenging field for making a clear-cut case that mouse experiments work, because researchers believe that both the phenomena they are studying and the animal models they are using are complex. These assumptions of complexity change the nature of what laboratory work produces. Whereas historical and ethnographic studies traditionally portray the laboratory as a place where scientists control, simplify, and stabilize nature in the service of producing durable facts, the laboratory that emerges from Nelson’s extensive interviews and fieldwork is a place where stable findings are always just out of reach. The ongoing work of managing precarious experimental systems means that researchers learn as much—if not more—about the impact of the environment on behavior as they do about genetics. Model Behavior offers a compelling portrait of life in a twenty-first-century laboratory, where partial, provisional answers to complex scientific questions are increasingly the norm.