2019-A Tutorial on IEEE 802.11ax High Efficiency WLANs.pdf

介绍wifi 6的知识 这是根据802.11ax Draft1.0和2.0写的tutorial文档！ 这是根据802.11ax Draft1.0和2.0写的tutorial文档！ 这是根据802.11ax Draft1.0和2.0写的tutorial文档！

IEEE 802.15.4-2020 这是目前最新的规范文档

IEEE 802.15.4-2020 这是目前最新的规范文档

【高清中文+英文+PPT】An Introduction to Statistical Learning（统计学习导论 - 基于R应用）

【高清中文扫描版本+英文原版+课程PPT】 An Introduction to Statistical Learning-with Applications in R（统计学习导论 - 基于R应用） 带有详细书签

【2018新书】Scala Machine Learning Projects（Scala机器学习项目）

【2018新书】Scala Machine Learning Projects－Build real－world machine learning and deep learning projects with Scala

【2018新书】Machine Learning－A Practical Approach on the Statistical Learning Theory

【2018新书】Machine Learning－A Practical Approach on the Statistical Learning Theory

贝叶斯思维统计建模的Python学习法

贝叶斯思维统计建模的Python学习法 贝叶斯思维统计建模的Python学习法

Bayesian Methods for Statistical Analysis，统计分析中的贝叶斯方法（2015年书籍）

Bayesian Methods for Statistical Analysis，统计分析中的贝叶斯方法（2015年书籍）

【2018新书】Pro Machine Learning Algorithms（机器学习算法）

This book is intended for data scientists and analysts who are interested in looking under the hood of various machine learning algorithms. This book will give you the confidence and skills when developing the major machine learning models and when evaluating a model that is presented to you.

【2019新书】Machine Learning Paradigms（机器学习例示）

At the dawn of the fourth Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relations. Synergies between physical, digital, biological and energy sciences and technologies, sewn together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering, natural, computer and information sciences, economics, business, commerce, environment, healthcare and life sciences. The book at hand explores some of the emerging scientific and technological areas in which data analytics arises as a need and, thus, may play a significant role in the years to come. Coming as the third volume under the general title Machine Learning Paradigms and following two related monographs, the book includes an editorial note (Chap. 1) and an additional twelve (12) chapters and is divided into five parts, namely: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics and (5) Theoretical Advances and Tools for Data Analytics. This research book is directed towards professors, researchers, scientists, engineers and students of all disciplines. We hope that they all will find it useful in their works and researches. We are grateful to the authors and the reviewers for their excellent contributions and visionary ideas. We are also thankful to Springer for agreeing to publish this book. Last, but not least, we are grateful to the Springer staff for their excellent work in producing this book.

【2018新书】Deep Learning Cookbook

Chapter 1 provides in-depth information about how neural networks function, where to get data from, and how to preprocess that data to make it easier to consume. Chapter 2 is about getting stuck and what to do about it. Neural nets are notoriously hard to debug and the tips and tricks in this chapter on how to make them behave will come in handy when going through the more project-oriented recipes in the rest of the book. If you are impatient, you can skip this chapter and go back to it later when you do get stuck. Chapters 3 through 15 are grouped around media, starting with text rocessing, followed by image processing, and finally music processing in Chapter 15. Each chapter describes one project split into various recipes. Typically a chapter will start with a data acquisition recipe, followed by a few recipes that build toward the goal of the chapter and a recipe on data visualization. Chapter 16 is about using models in production. Running experiments in notebooks is great, but ultimately we want to share our results with actual users and get our models run on real servers or mobile devices. This chapter goes through the options.