ZFS improves everything about systems administration. Once you peek under the hood, though, ZFS’ bewildering array of knobs and tunables can overwhelm anyone. ZFS experts can make their servers zing—and now you can, too, with FreeBSD Mastery: Advanced ZFS. This small book teaches you to: • Use boot environments to make the riskiest sysadmin tasks boring • Delegate filesystem privileges to users • Containerize ZFS datasets with jails • Quickly and efficiently replicate data between machines • split layers off of mirrors • optimize ZFS block storage • handle large storage arrays • select caching strategies to improve performance • manage next-generation storage hardware • identify and remove bottlenecks • build screaming fast database storage • dive deep into pools, metaslabs, and more! Whether you manage a single small server or international datacenters, simplify your storage with FreeBSD Mastery: Advanced ZFS. Table of Contents Chapter 0: Introduction Chapter 1: Boot Environments Chapter 2: Delegation and Jails Chapter 3: Sharing Datasets Chapter 4: Replication Chapter 5: ZFS Volumes Chapter 6: Advanced Hardware Chapter 7: Caches Chapter 8: Performance Chapter 9: Tuning Chapter 10: ZFS Potpourri

freebsd mastery zfs系列的第一本，非常实用，作者Michael Lucas。英文版。

CSS.Mastery.精通CSS.pdf中文高清扫描版

经典的CSS著作，网上都是jpg格式的，一张一张图片看着太累，我把它整合成了pdf格式，方便大家阅读

Preface I wrote this book to help machine learning practitioners, like you, get on top of linear algebra, fast. Linear Algebra Is Important in Machine Learning There is no doubt that linear algebra is important in machine learning. Linear algebra is the mathematics of data. It’s all vectors and matrices of numbers. Modern statistics is described using the notation of linear algebra and modern statistical methods harness the tools of linear algebra. Modern machine learning methods are described the same way, using the notations and tools drawn directly from linear algebra. Even some classical methods used in the field, such as linear regression via linear least squares and singular-value decomposition, are linear algebra methods, and other methods, such as principal component analysis, were born from the marriage of linear algebra and statistics. To read and understand machine learning, you must be able to read and understand linear algebra. Practitioners Study Linear Algebra Too Early If you ask how to get started in machine learning, you will very likely be told to start with linear algebra. We know that knowledge of linear algebra is critically important, but it does not have to be the place to start. Learning linear algebra first, then calculus, probability, statistics, and eventually machine learning theory is a long and slow bottom-up path. A better fit for developers is to start with systematic procedures that get results, and work back to the deeper understanding of theory, using working results as a context. I call this the top-down or results-first approach to machine learning, and linear algebra is not the first step, but perhaps the second or third. Practitioners Study Too Much Linear Algebra When practitioners do circle back to study linear algebra, they learn far more of the field than is required for or relevant to machine learning. Linear algebra is a large field of study that has tendrils into engineering, physics and quantum physics. There are also

Welcome to Long Short-Term Memory Networks With Python. Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment. They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text generation. LSTMs are very di↵erent to other deep learning techniques, such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs), in that they are designed specifically for sequence prediction problems. I designed this book for you to rapidly discover what LSTMs are, how they work, and how you can bring this important technology to your own sequence prediction problems.

Welcome Welcome to XGBoost With Python. This book is your guide to fast gradient boosting in Python. You will discover the XGBoost Python library for gradient boosting and how to use it to develop and evaluate gradient boosting models. In this book you will discover the techniques, recipes and skills with XGBoost that you can then bring to your own machine learning projects. Gradient Boosting does have a some fascinating math under the covers, but you do not need to know it to be able to pick it up as a tool and wield it on important projects to deliver real value. From the applied perspective, gradient boosting is quite a shallow field and a motivated developer can quickly pick it up and start making very real and impactful contributions. This is my goal for you and this book is your ticket to that outcome.

Preface Machine learning algorithms dominate applied machine learning. Because algorithms are such a big part of machine learning you must spend time to get familiar with them and really understand how they work. I wrote this book to help you start this journey. You can describe machine learning algorithms using statistics, probability and linear algebra. The mathematical descriptions are very precise and often unambiguous. But this is not the only way to describe machine learning algorithms. Writing this book, I set out to describe machine learning algorithms for developers (like myself). As developers, we think in repeatable procedures. The best way to describe a machine learning algorithm for us is: 1. In terms of the representation used by the algorithm (the actual numbers stored in a file). 2. In terms of the abstract repeatable procedures used by the algorithm to learn a model from data and later to make predictions with the model. 3. With clear worked examples showing exactly how real numbers plug into the equations and what numbers to expect as output. This book cuts through the mathematical talk around machine learning algorithms and shows you exactly how they work so that you can implement them yourself in a spreadsheet, in code with your favorite programming language or however you like. Once you possess this intimate knowledge, it will always be with you. You can implement the algorithms again and again. More importantly, you can translate the behavior of an algorithm back to the underlying procedure and really know what is going on and how to get the most from it. This book is your tour of machine learning algorithms and I’m excited and honored to be your tour guide. Let’s dive in.