Author: Luis F. Alvarez Leon Publisher: Univ of California Press ISBN: 0520389336 Category : Business & Economics Languages : en Pages : 221
Book Description
Digital technologies have changed how we shop, work, play, and communicate, reshaping our societies and economies. To understand digital capitalism, we need to grasp how advances in geospatial technologies underpin the construction, operation, and refinement of markets for digital goods and services. In The Map in the Machine, Luis F. Alvarez Leon examines these advances, from MapQuest and Google Maps to the rise of IP geolocation, ridesharing, and a new Earth Observation satellite ecosystem. He develops a geographical theory of digital capitalism centered on the processes of location, valuation, and marketization to provide a new vantage point from which to better understand, and intervene in, the dominant techno-economic paradigm of our time. By centering the spatiality of digital capitalism, Alvarez Leon shows how this system is the product not of seemingly intangible information clouds but rather of a vast array of technologies, practices, and infrastructures deeply rooted in place, mediated by geography, and open to contestation and change.
Author: Prateek Joshi Publisher: Packt Publishing Ltd ISBN: 1786467682 Category : Computers Languages : en Pages : 304
Book Description
100 recipes that teach you how to perform various machine learning tasks in the real world About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide Learn about perceptrons and see how they are used to build neural networks Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniques Who This Book Is For This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code. What You Will Learn Explore classification algorithms and apply them to the income bracket estimation problem Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Explore data visualization techniques to interact with your data in diverse ways Find out how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Analyze stock market data using Conditional Random Fields Work with image data and build systems for image recognition and biometric face recognition Grasp how to use deep neural networks to build an optical character recognition system In Detail Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples. Style and approach You will explore various real-life scenarios in this book where machine learning can be used, and learn about different building blocks of machine learning using independent recipes in the book.
Author: West Virginia. Legislature. Joint Select Committee to Investigate the Cause of Mine Explosions Publisher: ISBN: Category : Mine explosions Languages : en Pages : 856
Author: Joan Sweeney Publisher: Knopf Books for Young Readers ISBN: 152477202X Category : Juvenile Nonfiction Languages : en Pages : 32
Book Description
Maps can show you where you are anywhere in the world! A beloved bestseller that helps children discover their place on the planet, now refreshed with new art from Qin Leng. Where are you? Where is your room? Where is your home? Where is your town? This playful introduction to maps shows children how easy it is to find where they live and how they fit in to the larger world. Filled with fun and adorable new illustrations by Qin Leng, this repackage of Me on the Map will show readers how easy it is to find the places they know and love with help from a map.
Author: Christoph Molnar Publisher: Lulu.com ISBN: 0244768528 Category : Artificial intelligence Languages : en Pages : 320
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.
Author: Luis F. Alvarez Leon
Author: Gail Herman
Author: Gail Herman
Author: Prateek Joshi
Author: West Virginia. Legislature. Joint Select Committee to Investigate the Cause of Mine Explosions
Author: United States. Bureau of the Census
Author: Joan Sweeney
Author: 
Author: Christoph Molnar
Author: American Association of Instructors of the Blind