Table of Contents CHAPTER 1: WELCOME TO LINUX AND MACOS PART I: THE LINUX AND MACOS OPERATING SYSTEMS CHAPTER 2: GETTING STARTED CHAPTER 3: THE UTILITIES CHAPTER 4: THE FILESYSTEM CHAPTER 5: THE SHELL PART II: THE EDITORS CHAPTER 6: THE VIM EDITOR CHAPTER 7: THE EMACS EDITOR PART III: THE SHELLS CHAPTER 8: THE BOURNE AGAIN SHELL (bash) CHAPTER 9: THE TC SHELL (tcsh) PART IV: PROGRAMMING TOOLS CHAPTER 10: PROGRAMMING THE BOURNE AGAIN SHELL (bash) CHAPTER 11: THE PERL SCRIPTING LANGUAGE CHAPTER 12: THE PYTHON PROGRAMMING LANGUAGE CHAPTER 13: THE MARIADB SQL DATABASE MANAGEMENT SYSTEM CHAPTER 14: THE AWK PATTERN PROCESSING LANGUAGE CHAPTER 15: THE SED EDITOR PART V: SECURE NETWORK UTILITIES CHAPTER 16: THE RSYNC SECURE COPY UTILITY CHAPTER 17: THE OPENSSH SECURE COMMUNICATION UTILITIES PART VI: COMMAND REFERENCE PART VII: APPENDIXES APPENDIX A: REGULAR EXPRESSIONS APPENDIX B: HELP APPENDIX C: Keeping the System Up-to-Date APPENDIX D: MACOS NOTES

PRACTICAL MALWARE ANALYSIS The Hands-On Guide to Dissecting Malicious Software

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

ssd3 practical quiz 8ssd3 practical quiz 8ssd3 practical quiz 8ssd3 practical quiz 8ssd3 practical quiz 8ssd3 practical quiz 8

飞行控制系统设计方面的书籍~~

ssd3 Practical Quiz 5 答案 ssd3 Practical Quiz 5 答案

转载《A Practical Guide to Adopting the Universal Verification Methodology》 的中文版，不是《UVM实战》也不是《The UVM Primer》，翻译者不详

J.T. Betts 所著的讲述求解最优控制问题的实用数值方法的书，经典。

Practical Statistics for Data Scientist

intel资深专家。《A Practical Guide to TPM 2.0》一书的作者编写的ppt。