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这本书可是英美经典书籍。 What you need to know about data mining and data-analytic thinking

The book primarily addresses programming on a graphics processing unit (GPU) while covering some material also relevant to programming on a central processing unit (CPU). It discusses many concepts of general purpose GPU (GPGPU) programming and presents practical examples in game programming and scientific programming.

This whole book aims to bring ideas and algorithms together. I am convinced that they must be taught and learned in the same course. The algorithm clarifies the idea. The old method, separation of responsibilities , no longer works: Not perfect Mathematics courses teach analytical techniques Engineering courses work on real problems Even within computational science there is a separation we don't need: Not efficient Mathematics courses analyze numerical algorithms Engineering and computer science implement the software I believe it is time to teach and learn the reality of computational science and engineering. I hope this book helps to move that beautiful subj ect forward. Thank you for reading it .

R语言数据处理的经典教材，作图、数据处理结合，tidyverse

First release: 26 October 2017 www.sciencemag.org (Page numbers not final at time of first release) 1 The ability to learn and generalize from a few examples is a hallmark of human intelligence (1). CAPTCHAs, images used by websites to block automated interactions, are examples of problems that are easy for humans but difficult for comput-ers. CAPTCHAs are hard for algorithms because they add clutter and crowd letters together to create a chicken-and-egg problem for character classifiers — the classifiers work well for characters that have been segmented out, but segmenting the individual characters requires an understanding of the characters, each of which might be rendered in a combinato-rial number of ways (2–5). A recent deep-learning approach for parsing one specific CAPTCHA style required millions of labeled examples from it (6), and earlier approaches mostly relied on hand-crafted style-specific heuristics to segment out the character (3, 7); whereas humans can solve new styles without explicit training (Fig. 1A). The wide variety of ways in which letterforms could be rendered and still be under-stood by people is illustrated in Fig. 1. Building models that generalize well beyond their train-ing distribution is an important step toward the flexibility Douglas Hofstadter envisioned when he said that “for any program to handle letterforms with the flexibility that human beings do, it would have to possess full-scale artificial intelli-gence” (8). Many researchers have conjectured that this could be achieved by incorporating the inductive biases of the vis-ual cortex (9–12), utilizing the wealth of data generated by neuroscience and cognitive science research. In the mamma-lian brain, feedback connections in the visual cortex play roles in figure-ground-segmentation, and in object-based top-down attention that isolates the contours of an object even when partially transparent objects occupy the same spatial locations (13–16). Lateral connections in the visual co

Network science is becoming an increasingly valuable skill for both researchers and data scientists. Tools originally developed by sociologists and other researchers working with pen and paper have seen a resurgence as online platforms and social networks create huge datasets and advances in computer hardware make it feasible to analyze those data sets.

这是一篇来自science杂志的论文，非常经典！介绍了测地距离在流行降维中的应用。 Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: Þnding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputsÑ30,000 auditory nerve Þbers or 106 optic nerve ÞbersÑa manageably small number of perceptually relevant features. Here we describe an approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations, such as human handwriting or images of a face under different viewing conditions. In contrast to previous algorithms for nonlinear dimensionality reduction, ours efÞciently computes a globally optimal solution, and, for an important class of data manifolds, is guaranteed to converge asymptotically to the true structure.

The Science Of Programming

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of R for Data Science is to help you learn the most important tools in R that will allow you to do data science. After reading this book, you’ll have the tools to tackle a wide variety of data science challenges, using the best parts of R.