Brett Kennedy – författare

It's time we all stopped whining and learned a thing or two from The Toughest Cyclists Ever. Including:Stephen Roche, whose cure for exhaustion was to go up a gear and fight harder, all the way to the ambulance. Eddy Merckx, who hurt himself so badly in breaking the Hour record that, he estimated, he shortened his career by a year. Beryl Burton, who crushed her (male) rival's morale with the offer of a piece of liquorice, before speeding past to victory. Nicole Cooke and Edwig Van Hooydonck, who rejected dope and became legends.The Hardmen tells the stories - the good bits, anyway - of the 40 most heroic Cyclists ever. Their bravery, their panache and their Perfect Amount of Dumb. It reminds us that suffering on a bike liberates us from our daily lives, and that, in the words of Lance Armstrong "pain is temporary, quitting lasts forever"; proof that even assholes can be insightful.

Learn how to find the unusual, interesting, extreme, or inaccurate parts of your data.Outliers can be the most informative parts of your data, revealing hidden insights, novel patterns, and potential problems. For a business, this can mean finding new products, expanding markets, and flagging fraud or other suspicious activity. Outlier Detection in Python introduces the tools and techniques you'll need to uncover the parts of a dataset that don't look like the rest, even when they're the more hidden or intertwined among the expected bits.In Outlier Detection in Python you'll learn how to:

Use standard Python libraries to identify outliers Pick the right detection methods Combine multiple outlier detection methods for improved results Interpret your results Work with numeric, categorical, time series, and text data

Outlier detection (OD) is a vital tool for everything from financial auditing to network security. OD techniques also work for testing datasets for quality, collection errors, and data drift. This unique guide introduces the core tools of outlier detection like scikit-learn and PyOD, the principal algorithms used in outlier detection, and common pitfalls you might encounter.

Learn how to identify the unusual, interesting, extreme, or inaccurate parts of your data.Data scientists have two main tasks: finding patterns in data and finding the exceptions. These outliers are often the most informative parts of data, revealing hidden insights, novel patterns, and potential problems. Outlier Detection in Python is a practical guide to spotting the parts of a dataset that deviate from the norm, even when they''re hidden or intertwined among the expected data points. In Outlier Detection in Python you''ll learn how to: • Use standard Python libraries to identify outliers • Select the most appropriate detection methods • Combine multiple outlier detection methods for improved results • Interpret your results effectively • Work with numeric, categorical, time series, and text data Outlier detection is a vital tool for modern business, whether it''s discovering new products, expanding markets, or flagging fraud and other suspicious activities. This guide presents the core tools for outlier detection, as well as techniques utilizing the Python data stack familiar to data scientists. To get started, you''ll only need a basic understanding of statistics and the Python data ecosystem. About the technology Outliers—values that appear inconsistent with the rest of your data—can be the key to identifying fraud, performing a security audit, spotting bot activity, or just assessing the quality of a dataset. This unique guide introduces the outlier detection tools, techniques, and algorithms you’ll need to find, understand, and respond to the anomalies in your data. About the book Outlier Detection in Python illustrates the principles and practices of outlier detection with diverse real-world examples including social media, finance, network logs, and other important domains. You’ll explore a comprehensive set of statistical methods and machine learning approaches to identify and interpret the unexpected values in tabular, text, time series, and image data. Along the way, you’ll explore scikit-learn and PyOD, apply key OD algorithms, and add some high value techniques for real world OD scenarios to your toolkit. What''s inside • Python libraries to identify outliers • Combine outlier detection methods • Interpret your results About the reader For Python programmers familiar with tools like pandas and NumPy, and the basics of statistics. About the author Brett Kennedy is a data scientist with over thirty years’ experience in software development and data science. Table fo Contents Part 1 1 Introducing outlier detection 2 Simple outlier detection 3 Machine learning-based outlier detection 4 The outlier detection process Part 2 5 Outlier detection using scikit-learn 6 The PyOD library 7 Additional libraries and algorithms for outlier detection Part 3 8 Evaluating detectors and parameters 9 Working with specific data types 10 Handling very large and very small datasets 11 Synthetic data for outlier detection 12 Collective outliers 13 Explainable outlier detection 14 Ensembles of outlier detectors 15 Working with outlier detection predictions Part 4 16 Deep learning-based outlier detection 17 Time-series data

It''s time we all stopped whining and learned a thing or two from The Toughest Cyclists Ever. Including: Stephen Roche, whose cure for exhaustion was to go up a gear and fight harder, all the way to the ambulance. Eddy Merckx, who hurt himself so badly in breaking the Hour record that, he estimated, he shortened his career by a year. Beryl Burton, who crushed her (male) rival''s morale with the offer of a piece of liquorice, before speeding past to victory. Nicole Cooke and Edwig Van Hooydonck, who rejected dope and became legends. The Hardmen tells the stories - the good bits, anyway - of the 40 most heroic Cyclists ever. Their bravery, their panache and their Perfect Amount of Dumb. It reminds us that suffering on a bike liberates us from our daily lives, and that, in the words of Lance Armstrong "pain is temporary, quitting lasts forever"; proof that even assholes can be insightful.