But in the scalp of a balding man, they do not get everything they need and as a result, the hair-producing cells gradually die off. Here we have an example of a mild ‘disease’ which is caused by cellular malnutrition.” —Dr. Roger J. Williams “A living cell requires energy not only for all of its functions, but also for maintenance of its structure.” —Nobel Laureate Albert Szent-Györgyi "What could be more important to understand than biological energy? Thought, growth, movement, every philosophical and practical issue involves the nature of biological energy.” —Raymond Peat, PhD ======== The Current View of Pattern Hair Loss is Unproductive (and Dangerous) While it is often stated with great confidence that pattern pattern hair loss is the result of defective genes and "male" androgenic hormones (e.g., dihydrotestosterone or DHT), the theory is physiologically unsound. After 60 years of research the "genetic-androgen" hypoheses has produced a single FDA-approved "therapy" that works less than 50% the time and can result in permanent chemical castration (Minoxidil is a nonstarter for many men and women). In contrast, castrates and pseudohermaphrodites--who serve as the foundation for all baldness research--are protected from pattern hair loss 100% of the time. Steps Towards a 'Bioenergetic' View of Pattern Hair Loss Standing on the shoulders of giants (e.g., Otto Warburg, Albert Szent-Györgyi, Gilbert Ling, Ray Peat and others), HAIR LIKE A FOX sets up an alternative 'bioenergetic model' of pattern hair loss with a focus on the smallest unit of life, the cell. This same context elucidates simple yet effective therapies for halting and perhaps reversing pattern hair loss in a way that harmonizes with our unique physiology.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills