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Thank you for purchasing the MEAP for Deep Learning with R. If you are looking for a resource to learn about deep learning from scratch and to quickly become able to use this knowledge to solve real-world problems, you have found the right book. Deep Learning with R is meant for statisticians, analysts, engineers and students with a reasonable amount of R experience, but no significant knowledge of machine learning and deep learning. This book is an adaptation of my previously published Deep Learning with Python, with all of the code examples using the R interface to Keras. The goal of the book is to provide a learning resource for the R community that goes all the way from basic theory to advanced practical applications. Deep learning is an immensely rich subfield of machine learning, with powerful applications ranging from machine perception to natural language processing, all the way up to creative AI. Yet, its core concepts are in fact very simple. Deep learning is often presented as shrouded in a certain mystique, with references to algorithms that “work like the brain”, that “think” or “understand”. Reality is however quite far from this science- fiction dream, and I will do my best in these pages to dispel these illusions. I believe that there are no difficult ideas in deep learning, and that’s why I started this book, based on premise that all of the important concepts and applications in this field could be taught to anyone, with very few prerequisites. This book is structured around a series of practical code examples, demonstrating on real- world problems every the notions that gets introduced. I strongly believe in the value of teaching using concrete examples, anchoring theoretical ideas into actual results and tangible code patterns. These examples all rely on Keras, the deep learning library. When I released the initial version of Keras almost two years ago, little did I know that it would quickly skyrocket to become one of the most widely used deep learning frameworks. A big part of that success is that Keras has always put ease of use and accessibility front and center. This same reason is what makes Keras a great library to get started with deep learning, and thus a great fit for this book. By the time you reach the end of this book, you will have become a Keras expert. I hope that you will this book valuable —deep learning will definitely open up new intellectual perspectives for you, and in fact it even has the potential to transform your career, being the most in-demand scientific specialization these days. I am looking forward to your reviews and comments. Your feedback is essential in order to write the best possible book, that will benefit the greatest number of people.

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（2） 信号间互模糊函数分析 MIMO 雷达的接收端收到的回波信号包含所有的正交发射信号分量。我们可以采 用匹配滤波进行分离。但是匹配滤波实际上是对接收信号滑动相关的过程，因而其它 信号分量也将对匹配滤波产生影响，等效为信号间的互相关性能，因而可以用雷达互 相关函数来表示。因此，本节我们首先对信号互模糊函数进行分析。 设两个正交信号 ( )ms t ， ( )ns t 分别为 2 0 1 2 ( ) 2() )( pj f t t m f t ms t eu t       2-19 2 0 1 2 ( ) 2() )( pj f t t n f t ns t eu t       2-20 则 ( )ms t 与 ( )ns t 的互模糊函数为： * 2( , ) ( ) ( ) j tc m ns t s t e dt          2-21 由  2-19 ，  2-20 式可以得到 2 0 1 2 ( ) 2 ( ) 202 1 ( , ) p b bj f nf j t j t c a a p p c e e dt e dt T T                  2-22 其中 2 0 1 2 ( ) 2 0 pj f nf c e        2-23 ( ) pm n f     2-24 ( ) pm n f       2-25 确定积分限后，可以化解得到

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