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Preface I wrote this book to make the principles of wireless communication more accessible. Wireless communication is the dominant means of Internet access for most people, and it has become the means by which our devices connect to the Internet and to each other. Despite the ubiquity of wireless, the principles of wireless communication have remained out of reach for many engineers. The main reason seems to be that the technical concepts of wireless communication are built on the foundations of digital communication. Unfortunately, digital communication is normally studied at the end of an undergraduate program in electrical engineering, leaving no room for a course on wireless communication. In addition, this puts wireless communication out of reach for students in related areas like computer science or aerospace engineering, where digital communication may not be offered. This book provides a means to learn wireless communication together with the fundamentals of digital communication. The premise of this book is that wireless communication can be learned with only a background in digital signal processing (DSP). The utility of a DSP approach stems from the following fact: wireless communication signals (at least ideally) are bandlimited. Thanks to Nyquist’s theorem, it is possible to represent bandlimited continuous-time signals from their samples in discrete time. As a result, discrete time can be used to represent the continuous-time transmitted and received signals in a wireless system. With this connection, channel impairments like multipath fading and noise can be written in terms of their discrete-time equivalents, creating a model for the received signal that is entirely in discrete time.

This is the first definitive introduction to behavioral economics aimed at advanced undergraduate and postgraduate students. Authoritative, cutting edge, yet accessible, it guides the reader through theory and evidence, providing engaging and relevant applications throughout. It is divided into nine parts and 24 chapters: Part I is on behavioral economics of risk, uncertainty, and ambiguity. The evidence against expected utility theory is examined, and the behavioral response is outlined; the best empirically supported theory is prospect theory. Part II considers other-regarding preferences. The evidence from experimental games on human sociality is given, followed by models and applications of inequity aversion, intentions based reciprocity, conditional cooperation, human virtues, and social identity. Part III is on time discounting. It considers the evidence against the exponential discounted utility model and describes several behavioral models such as hyperbolic discounting, attribute based models and the reference time theory. Part IV describes the evidence on classical game theory and considers several models of behavioral game theory, including level-k and cognitive hierarchy models, quantal response equilibrium, and psychological game theory. Part V considers behavioral models of learning that include evolutionary game theory, classical models of learning, experience weighted attraction model, learning direction theory, and stochastic social dynamics. Part VI studies the role of emotions; among other topics it considers projection bias, temptation preferences, happiness economics, and interaction between emotions and cognition. Part VII considers bounded rationality. The three main topics considered are judgment heuristics and biases, mental accounting, and behavioral finance. Part VIII considers behavioral welfare economics; the main topics are soft paternalism, and choice-based measures of welfare. Finally, Part IX gives an abbreviated taster course in neuroeconomics.

In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered

算法第四版(普林斯顿大学)PPT“ I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships. ”

[统计信号处理基础——实用算法开发（卷III）][罗鹏飞 等][光盘资料].rar

If you have not installed NumPy or SciPy yet, you can also install these using conda or pip. When using pip, please ensure that binary wheels are used, and NumPy and SciPy are not recompiled from source, which can happen when using particular configurations of operating system and hardware (such as Linux on a Raspberry Pi). Building numpy and scipy from source can be complex (especially on Windows) and requires careful configuration to ensure that they link against an optimized implementation of linear algebra routines. Instead, use a third-party distribution as described below.

清晰 彩色 When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here. In fact, it has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). But the first ML application that really became mainstream, improving the lives of hundreds of millions of people, took over the world back in the 1990s: it was the spam filter. Not exactly a self-aware Skynet, but it does technically qualify as Machine Learning (it has actually learned so well that you seldom need to flag an email as spam anymore). It was followed by hundreds of ML applications that now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search.