Nonlinear Transistor Model Parameter Extraction Techniques 剑桥 Achieve accurate and reliable parameter extraction using this complete survey of stateof- the-art techniques and methods. A team of experts from industry and academia provides you with insights into a range of key topics, including parasitics, instrinsic extraction, statistics, extraction uncertainty, nonlinear and DC parameters, self-heating and traps, noise, and package effects. Learn how similar approaches to parameter extraction can be applied to different technologies. A variety of real-world industrial examples and measurement results show you how the theories and methods presented can be used in practice. Whether you use transistor models for evaluation of device processing, need to understand the methods behind the models you use in circuit design, or you want to develop models for existing and new device types, this is your complete guide to parameter extraction.

Quantitative Risk Management Concepts, Techniques and Tools Alexander J. McNeil R¨udiger Frey Paul Embrechts 2005 by Princeton University Press Princeton and Oxford

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该文档从以下几个方面进行解析： 1、Vivado基本操作流程 2、时序基本概念 3、时序基本约束和流程 4、Baselining时序约束 5、CDC时序约束 6、I/O时序 7、例外时序约束 8、时序收敛优化技术

It is about established, well-understood methods that are at the core of many of today's blockbusters. This book is meant to help you build a foundation layer, so you can understand all the base algorithms that, with minimal differences, are found in most game projects.

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The Machine Learning Pipeline 10 Data 11 Tasks 11 Models 12 Features 13 2. Basic Feature Engineering for Text Data: Flatten and Filter. . . . . . . . . . . . . . . . . . . . . . . 15 Turning Natural Text into Flat Vectors 15 Bag-of-words 16 Implementing bag-of-words: parsing and tokenization 20 Bag-of-N-Grams 21 Collocation Extraction for Phrase Detection 23 Quick summary 26 Filtering for Cleaner Features 26 Stopwords 26 Frequency-based filtering 27 Stemming 30 Summary 31 3. The Effects of Feature Scaling: From Bag-of-Words to Tf-Idf. . . . . . . . . . . . . . . . . . . . . . . 33 Tf-Idf : A Simple Twist on Bag-of-Words 33 Feature Scaling 35 Min-max scaling 35 Standardization (variance scaling) 36 L2 normalization 37 iii www.it-ebooks.info Putting it to the Test 38 Creating a classification dataset 39 Implementing tf-idf and feature scaling 40 First try: plain logistic regression 42 Second try: logistic regression with regularization 43 Discussion of results 46 Deep Dive: What is Happening? 47

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蒙特卡罗（Monte Carlo）算法的基础概念，很好的入门教程