概率图模型是机器学习中的一种技术，它利用图论的概念来简明地表示和优化预测数据问题中的值。 图形模型为我们提供了在数据中寻找复杂模式的技术，并广泛应用于语音识别、信息提取、图像分割和基因调控网络建模等领域。 本书从概率论和图论的基础出发，接着讨论各种模型和推理算法。讨论了所有不同类型的模型以及创建和修改模型的代码示例，并对它们运行了不同的推理算法。有一整章将继续介绍朴素的贝叶斯模型和隐藏的马尔可夫模型。这些模型已经用实际例子进行了深入的讨论。

Mastering Xamarin UI Development Second Edition, 共628页。

Mastering Spring Cloud 英文无水印转化版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

MASTERING HADOOP 3

Mastering Windows Presentation Foundation 英文epub 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

此为正版电子书，完整高清，并且内容都是可以复制的。 很经典的书，可以称为kvm的圣经。

Mastering Windows Server 2019 - Second Edition covers all of the essential information needed to implement and utilize this latest-and-greatest platform as the core of your data center computing needs. You will begin by installing and managing Windows Server 2019, and by clearing up common points of confusion surrounding the versions and licensing of this new product. Centralized management, monitoring, and configuration of servers is key to an efficient IT department, and you will discover multiple methods for quickly managing all of your servers from a single pane of glass. To this end, you will spend time inside Server Manager, PowerShell, and even the new Windows Admin Center, formerly known as Project Honolulu. Even though this book is focused on Windows Server 2019 LTSC, we will still discuss containers and Nano Server, which are more commonly related to the SAC channel of the server platform, for a well-rounded exposition of all aspects of using Windows Server in your environment. We also discuss the various remote access technologies available in this operating system, as well as guidelines for virtualizing your data center with Hyper-V. By the end of this book, you will have all the ammunition required to start planning for, implementing, and managing Windows.

Mastering TensorFlow 1.x

本书是Mastering Technical Sales的英文第三版。 Every high-tech sales team today has technical pros on board to "explain how things work," and this success-tested training resource is written just for them. This newly revised and expanded third edition of an Artech House bestseller offers invaluable insights and tips for every stage of the selling process. This third edition features a wealth of new material, including new chapters on business-driven discovery, white boarding, trusted advisors, and calculating ROI. This invaluable book equips new sales engineers with powerful sales and presentation techniques that capitalize on their technical background - all spelled out step-by-step by a pair of technical sales experts with decades of eye-popping, industry-giant success under their belt.

Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work