Peter Skomoroch

A groundbreaking examination of human perception, reality and the evolutionary schism between the two Do we see the world as it truly is? In The Case Against Reality, pioneering cognitive scientist Donald Hoffman says no? we see what we need in order to survive. Our visual perceptions are not a window onto reality, Hoffman shows us, but instead are interfaces constructed by natural selection. The objects we see around us are not unlike the file icons on our computer desktops: while shaped like a small folder on our screens, the files themselves are made of a series of ones and zeros - too complex for most of us to understand. In a similar way, Hoffman argues, evolution has shaped our perceptions into simplistic illusions to help us navigate the world around us. Yet now these illusions can be manipulated by advertising and design. Drawing on thirty years of Hoffman's own influential research, as well as evolutionary biology, game theory, neuroscience, and philosophy, The Case Against Reality makes the mind-bending yet utterly convincing case that the world is nothing like what we see through our eyes.

“One of the best critiques of current K-12 mathematics education I have ever seen, written by a first-class research mathematician who elected to devote his teaching career to K-12 education.” ―Keith Devlin, NPR’s “Math Guy” A brilliant research mathematician reveals math to be a creative art form on par with painting, poetry, and sculpture, and rejects the standard anxiety-producing teaching methods used in most schools today. Witty and accessible, Paul Lockhart’s controversial approach will provoke spirited debate among educators and parents alike, altering the way we think about math forever. Paul Lockhart is the author of Arithmetic, Measurement, and A Mathematician’s Lament. He has taught mathematics at Brown University, University of California, Santa Cruz, and to K-12 level students at St. Ann’s School in Brooklyn, New York.

Directing his polemics against the pedantry of his time, Galileo, as his own popularizer, addressed his writings to contemporary laymen. His support of Copernican cosmology, against the Church's strong opposition, his development of a telescope, and his unorthodox opinions as a philosopher of science were the central concerns of his career and the subjects of four of his most important writings. Drake's introductory essay place them in their biographical and historical context.

This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 100 Unix power tools--useful whether you work with Windows, macOS, or Linux. You'll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you're comfortable processing data with Python or R, you'll learn how to greatly improve your data science workflow by leveraging the command line's power. This book is ideal for data scientists, analysts, engineers, system administrators, and researchers. Obtain data from websites, APIs, databases, and spreadsheetsPerform scrub operations on text, CSV, HTML, XML, and JSON filesExplore data, compute descriptive statistics, and create visualizationsManage your data science workflowCreate your own tools from one-liners and existing Python or R codeParallelize and distribute data-intensive pipelinesModel data with dimensionality reduction, regression, and classification algorithmsLeverage the command line from Python, Jupyter, R, RStudio, and Apache Spark

Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Summary Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. About the book Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You’ll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You’ll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows. What's inside Identifying the right training and evaluation data Finding and managing people to annotate data Selecting annotation quality control strategies Designing interfaces to improve accuracy and efficiency About the author Robert (Munro) Monarch is a data scientist and engineer who has built machine learning data for companies such as Apple, Amazon, Google, and IBM. He holds a PhD from Stanford. Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional in addition to being a machine learning professional. A worked example throughout this text is classifying disaster-related messages from real disasters that Robert has helped respond to in the past. Table of Contents PART 1 - FIRST STEPS 1 Introduction to human-in-the-loop machine learning 2 Getting started with human-in-the-loop machine learning PART 2 - ACTIVE LEARNING 3 Uncertainty sampling 4 Diversity sampling 5 Advanced active learning 6 Applying active learning to different machine learning tasks PART 3 - ANNOTATION 7 Working with the people annotating your data 8 Quality control for data annotation 9 Advanced data annotation and augmentation 10 Annotation quality for different machine learning tasks PART 4 - HUMAN–COMPUTER INTERACTION FOR MACHINE LEARNING 11 Interfaces for data annotation 12 Human-in-the-loop machine learning products

Traces the relationship between the development of computing machines and our knowledge of brain functioning, and introduces corresponding mathematical models designed to describe this relationship. Begins with a historical overview tracing the rise of cybernetics to the current interchange of ideas between AI and brain theory. Subsequent chapters introduce neural sets and finite automata, the crucial cybernetic concepts of feedback and realization, pattern recognition networks, "semi-neural" learning networks, capabilities of Turing machines and automata which construct as well as compute. The final chapter presents two accessible proofs of Gödel's Incompleteness Theorem.

The best and most successful guide to reading comprehension for the general reader, completely rewritten and updated with new material. A CNN Book of the Week: “Explains not just why we should read books, but how we should read them. It's masterfully done.” —Farheed Zakaria Originally published in 1940, this book is a rare phenomenon, a living classic that introduces and elucidates the various levels of reading and how to achieve them—from elementary reading, through systematic skimming and inspectional reading, to speed reading. Readers will learn when and how to “judge a book by its cover,” and also how to X-ray it, read critically, and extract the author’s message from the text. Also included is instruction in the different techniques that work best for reading particular genres, such as practical books, imaginative literature, plays, poetry, history, science and mathematics, philosophy and social science works. Finally, the authors offer a recommended reading list and supply reading tests you can use measure your own progress in reading skills, comprehension, and speed.