自己做的课后编程练习，都顺利通过了，且有不同解法，对初学的同学不失为一个参考

Structured Learning Unified Framework Unified Framework – Object Detection Unified Framework - Summarization Unified Framework - Retrieval Structured Linear Model

Learning A Bayesian and Optimization Perspective 英文原版书籍，高清带目录，非扫描版

Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement. I believe that any machine learning practitioner should be proficient in statistics as well as in mathematics, so that they can speculate and solve any machine learning problem in an efficient manner. In this book, we will cover the fundamentals of statistics and machine learning, giving you a holistic view of the application of machine learning tech niques for relevant problems. We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming. We will use libraries such as scikit-learn, e1071, randomForest, c50, xgboost, and so on. We will also go over the fundamentals of deep learning with the help of Keras software. Furthermore, we will have an overview of reinforcement learning with pure Python programming language. The book is motivated by the following goals: To help newbies get up to speed with various fundamentals, whilst also allowing experienced professionals to refresh their knowledge on various concepts and to have more clarity when applying algorithms on their chosen data. To give a holistic view of both Python and R, this book will take you through various examples using both languages. To provide an introduction to new trends in machine learning, fundamentals of deep learning and reinforcement learning are covered with suitable examples to teach you state of the art techniques.

The Hundred-Page Machine Learning Book 原版英文书籍前3章；作者：Andriy Burkov

决策树和随机森林-邹伟的ppt，适用于机器学习爱好者。

分位数回归森林论文，基于随机森林，可进行分位数回归预测

Advances in Financial Machine Learning By 作者: Marcos Lopez de Prado ISBN-10 书号: 1119482089 ISBN-13 书号: 9781119482086 Edition 版本: 1 出版日期: 2018-02-21 pages 页数: (400) $50 Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Hands on Machine Learning with Scikit Learn and TensorFlow 英文原版 彩色配图

Unsupervised Learning: Deep Auto-encoder Auto-encoder Deep Auto-encoder Auto-encoder – Text Retrieval Auto-encoder – Similar Image Search Auto-encoder for CNN CNN -Unpooling CNN - Deconvolution