Implementing a Language with LLVM in Objective Caml-llvm.org (2017).pdf

The goal of this tutorial is to progressively unveil our language, describing how it is built up over time. This will let us cover a fairly broad range of language design and LLVM-specific usage issues, showing and explaining the code for it all along the way, without overwhelming you with tons of details up front

Mayur Pandey, Suyog Sarda - LLVM Cookbook-Packt Publishing (2015).pdf

This book travels through all the phases of compilation: frontend processing, code optimization, code emission, and so on. And to make this journey easy, LLVM is the simplest compiler infrastructure to study. It's a modular, layered compiler infrastructure where every phase is dished out as a separate recipe. Written in object-oriented C++, LLVM gives programmers a simple interface and lots of APIs to write their own compiler.

Modern C++ Programming Cookbook[May 2017].pdf.zip

This book addresses many of the new features included in C++11, C++14, and the forthcoming C++17. This book is organized in recipes, each covering one particular language or library feature, or a common problem developers face and its typical solution using modern C++. Through more than 100 recipes, you will learn to master both core language features and the standard libraries, including those for strings, containers, algorithms, iterators, input/output, regular expressions, threads, filesystem, atomic operations, and utilities.

Julia-Cookbook

programming language for data science and its applications. It also serves as a guide to handle data in the most available formats, and shows how to crawl and scrape data from the Internet. Chapter 2, Metaprogramming, covers the concept of metaprogramming, where a language can express its own code as a data structure of itself. For example, Lisp expresses code in the form of Lisp arrays, which are data structures in Lisp itself. Similarly, Julia can express its code as data structures. Chapter 3, Statistics with Julia, teaches you how to perform statistics in Julia, along with the common problems of handling data arrays, distributions, estimation, and sampling techniques. Chapter 4, Building Data Science Models, talks about various data science and statistical models. You will learn to design, customize, and apply them to various data science problems. This chapter will also teach you about model selection and the ways to learn how to build and understand robust statistical models. Chapter 5, Working with Visualizations, teaches you how to visualize and present data, and also to analyze and the findings from the data science approach that you have taken to solve a particular problem. There are various types of visualizations to display your findings, namely the bar plot, the scatter plot, pie chart, and so on. It is very important to choose an appropriate method that can reflect your findings and work in a sensible and an aesthetically pleasing manner. Chapter 6, Parallel Computing, talks about the concepts of parallel computing and handling a lot of data in Julia.

Getting-Started-with-Julia-Programming

Chapter 1, Installing the Julia Platform, guides you with the installation of all the necessary components required for a Julia environment. It teaches you how to work with Julia’s console (the REPL) and discusses some of the more elaborate development editors you can use. Chapter 2, Variables, Types, and Operations, discusses the elementary built-in types in Julia, and the operations that can be performed on them, so that you are prepared to start writing the code with them. Chapter 3, Functions, explains why functions are the basic building blocks of Julia, and how to effectively use them. Chapter 4, Control Flow, shows Julia’s elegant control constructs, how to perform error handling, and how to use coroutines (called Tasks in Julia) to structure the execution of your code. Chapter 5, Collection Types, explores the different types that group individual values, such as arrays and matrices, tuples, dictionaries, and sets. Chapter 6, More on Types, Methods, and Modules, digs deeper into the type concept and explains how this is used in multiple dispatch to get C-like performance. Modules, a higher code organizing concept, are discussed as well. Chapter 7, Metaprogramming in Julia, touches on the deeper layers of Julia, such as expressions and reflection capabilities, and demonstrates the power of macros. Chapter 8, I/O, Networking, and Parallel Computing, shows how to work with data in files and databases using DataFrames. We can explore the networking capabilities, and shows how to set up a parallel computing environment with Julia. Chapter 9, Running External Programs, looks at how Julia interacts with the command line and other languages and also discusses performance tips. Chapter 10, The Standard Library and Packages, digs deeper into the standard library and demonstrates the important packages for visualization of data.

Julia-High-performance

Chapter 1, Julia is Fast, discuses some of the design underpinning the language and its focus on high performance. Chapter 2, Analyzing Julia Performance, provides the tools and techniques you can use to measure and analyze the performance of your own programs. Chapter 3, Types in Julia, describes the type system and discusses why writing type-stable code is crucial to high performance. Chapter 4, Functions and Macros – Structuring Julia Code for High Performance, discusses techniques to use dispatch and code generation to structure high-performance programs. Chapter 5, Fast Numbers, discusses the basic numeric types and why they are fast.

Pro Deep Learning with TensorFlow.

Pro Deep Learning with TensorFlow is a practical and mathematical guide to deep learning using TensorFlow. Deep learning is a branch of machine learning where you model the world in terms of a hierarchy of concepts. This pattern of learning is similar to the way a human brain learns, and it allows computers to model complex concepts that often go unnoticed in other traditional methods of modeling. Hence, in the modern computing paradigm, deep learning plays a vital role in modeling complex real-world problems, especially by leveraging the massive amount of unstructured data available today

NIPS会议全文下载链接

编写python代码（http://blog.csdn.net/shouhuxianjian/article/details/78075431），然后将NIPS会议从1988年到2016年所有论文的下载链接保存在对应的文件夹 下面的urls.txt中，可以直接复制下载链接到迅雷中，从而

Tree Boosting With XGBoost

Tree boosting has empirically proven to be a highly effective approach to predictive modeling. It has shown remarkable results for a vast array of problems. For many years, MART has been the tree boosting method of choice. More recently, a tree boosting method known as XGBoost has gained popularity by winning numerous machine learning competitions. In this thesis, we will investigate how XGBoost differs from the more traditional MART. We will show that XGBoost employs a boosting algorithm which we will term Newton boosting. This boosting algorithm will further be compared with the gradient boosting algorithm that MART employs. Moreover, we will discuss the regularization techniques that these methods offer and the effect these have on the models.

XGBoost A Scalable Tree Boosting System.

Tree boosting is a highly eective and widely used machine learning method. In this paper, we describe a scalable endto- end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.