射频振荡电路设计参考；尤其对于晶体振荡电路的分析，如皮尔斯振荡电路等电路的分析设计具有一定的参考价值

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Kavya Joshi - Understanding Channels；理解Channels

非扫描、高清版本 UNDERSTANDING MACHINE LEARNING From Theory to Algorithms Shai Shalev-Shwartz The Hebrew University, Jerusalem Shai Ben-David University of Waterloo, Canada

Understanding the Linux Kernel (3rd Edition) 非常经典的linux内核学习资料！

关于Delta+Sigma ADC原理，过采样的参考书.可以参考

Understanding UNIX LINUX Programming

一个经典的LSTM教程，以图形化方式开始，从RNN开始，逐步引入Cell的思想和各种门的思想。 Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence. Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones. Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.

资源不错，主要免积分，需要的共享

JESD204B相关资源，对于开发JESD204B的同学来说是必看的资料