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The Quick Python Book. Python 经典图书， 清晰文字源生PDF，带目录标签。2018年最新第三版。

The Kubernetes Book，2019年3月版，包含 - Kubernetes cluster architecture - How to build Kubernetes Clusters - How to deploy and manage applications on Kubernetes - How to secure Kubernetes - The meaning of terms like cloud-native, microservices, desired state, containerized, and more...

学习Python requests（丢掉urllib2吧）最好最新的书，含pdf， epub两个格式，电脑手机上同时学习用

此书包含HFSS的基本原理，使用方法以及大量实例，能让新手快速入门！

Learning Data Mining With Python book 代码及实例数据

自适应滤波器教材PDF (英文第三版994页+中文第四版745页)

ethereum_book_精通以太坊 （中文版） 此书把以太坊原理、智能合约、ERP20等相关知识点进行了详细的讲解，并附有相关代码

Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring data sets, as well as, for building predictive models. The main parts of the book include: Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. Model validation and evaluation techniques for measuring the performance of a predictive model. Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: Covers machine learning algorithm and implementation Key mathematical concepts are presented Short, self-contained cha

这是一本关于 Fragment Shaders（片段着色器）的入门指南，它将一步一步地带你领略其中的纷繁与抽象, 作者Patricio Gonzalez Vivo和Jen Lowe