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Chapter 3: Linear Methods for Regression
Linear regression is one of the oldest and most important statistical learning methods. The linear model makes strong assumptions about the structure of the data, and when these assumptions are met...
We discuss least squares fitting, subset selection, shrinkage methods, and methods using derived input features to improve the linear model's performance.
Chapter 2: Meaningful Names
Writing clean code is a skill that separates the professional from the amateur. Clean code is not written by following a set of rules. You don't become a software craftsman by learning a list of heuristics...
This book teaches you how to write code that is expressive, readable, and maintainable. You'll learn the difference between good and bad code, and how to transform bad code into good code.
Abstract
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also...
We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.
Executive Summary
As AI systems become more powerful and widespread, ensuring their safety and alignment with human values becomes increasingly critical. This report examines current risks...
We analyze governance frameworks, technical safety measures, and international cooperation efforts to mitigate potential harms from advanced AI systems.
Introduction to Deep Learning
Deep learning has revolutionized artificial intelligence by enabling machines to learn complex patterns from data. Neural networks with multiple hidden layers can approximate any function...
We'll explore convolutional networks for vision, recurrent networks for sequences, and transformer architectures that power modern AI systems.
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