In the fourteen years between his time in New York with value-investing guru Benjamin Graham and his start as chairman of Berkshire Hathaway, Warren Buffett managed Buffett Partnership Limited, his first professional investing partnership. Over the course of that time—a period in which he experienced an unprecedented record of success—Buffett wrote semiannual letters to his small but growing group of partners, sharing his thoughts, approaches, and reflections.
Compiled for the first time and with Buffett’s permission, the letters spotlight his contrarian diversification strategy, his almost religious celebration of compounding interest, his preference for conservative rather than conventional decision making, and his goal and tactics for bettering market results by at least 10% annually. Demonstrating Buffett’s intellectual rigor, they provide a framework to the craft of investing that had not existed before: Buffett built upon the quantitative contributions made by his famous teacher, Benjamin Graham, demonstrating how they could be applied and improved.
Jeremy Miller reveals how these letters offer us a rare look into Buffett’s mind and offer accessible lessons in control and discipline—effective in bull and bear markets alike, and in all types of investing climates—that are the bedrock of his success. Warren Buffett’s Ground Rules paints a portrait of the sage as a young investor during a time when he developed the long-term value-oriented strategy that helped him build the foundation of his wealth—rules for success every investor needs today.
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
Artificial Intelligence helps choose what books you buy, what movies you see, and even who you date. It puts the "smart" in your smartphone and soon it will drive your car. It makes most of the trades on Wall Street, and controls vital energy, water, and transportation infrastructure. But Artificial Intelligence can also threaten our existence.
In as little as a decade, AI could match and then surpass human intelligence. Corporations and government agencies are pouring billions into achieving AI's Holy Grail—human-level intelligence. Once AI has attained it, scientists argue, it will have survival drives much like our own. We may be forced to compete with a rival more cunning, more powerful, and more alien than we can imagine.
Through profiles of tech visionaries, industry watchdogs, and groundbreaking AI systems, Our Final Invention explores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to?