A Practical Way
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A Practical Way to Get Rich . . . and Die Trying
Author: John Roa
language: en
Publisher: National Geographic Books
Release Date: 2021-09-07
"A scathingly honest memoir of entrepreneurship's dark reality... I would advise every entrepreneur--or anyone who dreams of becoming one--to read this book." --Eric Schurenberg - CEO, Fast Company and Inc. A young tech entrepreneur's memoir of building his hugely successful company and the mental and physical price he paid for it At the age of twenty-six, John Roa founded a tech-consulting and design firm that he sold for a fortune to the largest tech company in San Francisco, Salesforce. His account of his rise from a self-described below-average student, to becoming a poster boy for the ambitious, successful young entrepreneur, to nearly destroying himself in the process is the subject of A Practical Way to Get Rich . . . and Die Trying. Roa's twenty-year-long journey from being dead-broke to wealth he never imagined is an absurd and often comical story of talent, luck, risk, rapidly changing technology, larger-than-life personalities, sex, gambling, and excessive alcohol and drug consumption. Roa's intention for his memoir is not to present a glamorous rags-to-riches saga, but, instead, to serve as a cautionary tale of the toll that entrepreneurship can take on ambitious young people unprepared for the physical and mental costs that "making it" can take. Those pitfalls eventually took their toll on Roa, who, in the face of round-the-clock pressure and risk taking, ultimately suffered a psychotic breakdown from which he almost didn't walk away. As he healed in the aftermath, he began to question the ethos that had brought him to that dark place, and he learned from other entrepreneurs that they, too, had experienced similar debilitating issues that they felt unable to admit, let alone discuss. A Practical Way to Get Rich . . . and Die Trying is a compelling memoir and the foundation for a campaign of honesty and vulnerability in an industry that currently allows neither. Roa aims to be the bridge to helping young leaders confront the mental health issues and abuse that too often accompany the tech startup that so many have embraced as their salvation for their future.
Machine Learning
This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.
Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala