Andrew Smart is the author of Autopilot: The Art and Science of Doing Nothing. A scientist and engineer interested in consciousness, brains and technology, his work traverses the boundaries of neuroscience, philosophy, culture, radical politics and metaphysics. He was raised in the U.S., educated and married in Sweden, lived in New York and Minneapolis and now lives in Switzerland.
At every turn we’re pushed to do more, faster and more efficiently: that drumbeat resounds throughout our wage-slave society. Multitasking is not only a virtue, it’s a necessity. Books such as Getting Things Done, The One Minute Manager, and The 7 Habits of Highly Effective People regularly top the bestseller lists, and have spawned a considerable industry.
But Andrew Smart argues that slackers may have the last laugh. The latest neuroscience shows that the “culture of effectiveness” is not only ineffective, it can be harmful to your well-being. He makes a compelling case – backed by science – that filling life with activity at work and at home actually hurts your brain.
A survivor of corporate-mandated “Six Sigma” training to improve efficiency, Smart has channeled a self-described “loathing” of the time-management industry into a witty, informative and wide-ranging book that draws on the most recent research into brain power. Use it to explain to bosses, family, and friends why you need to relax – right now.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details