A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) ISBN-10 书号: 1138744387 ISBN-13 书号: 9781138744387 Edition 版本: 1 出版日期: 2018-04-04 pages 页数: 418 Chapter 1 Preliminaries and Overview Guozhu Dong and Huan Liu Part I Feature Engineering for Various Data Types Chapter 2 Feature Engineering for Text Data Chase Geigle, Qiaozhu Mei, and ChengXiang Zhai Chapter 3 Feature Extraction and Learning for Visual Data Parag S. Chandakkar, Ragav Venkatesan, and Baoxin Li Chapter 4 Feature-Based Time-Series Analysis Ben D. Fulcher Chapter 5 Feature Engineering for Data Streams Yao Ma, Jiliang Tang, and Charu Aggarwal Chapter 6 Feature Generation and Feature Engineering for Sequences Guozhu Dong, Lei Duan, Jyrki Nummenmaa, and Peng Zhang Chapter 7 Feature Generation for Graphs and NetworksYuan Yao, Hanghang Tong, Feng Xu, and Jian Lu Part lI General Feature Engineering Techniques Chapter 8 Feature Selection and Evaluation Yun Li and Tao Li Chapter 9 Automating Feature Engineering in Supervised Learning Udayan Khurana Chapter 10 Pattern-Based Feature Generation Yunzhe Jia, James Bailey, Ramamohanarao Kotagiri, and Christopher Leckie Chapter 11 Deep Learning for Feature Representation Suhang Wang and Huan Liu Part ll Feature Engineering in Special Applications Chapter 12 Feature Engineering for Social Bot Detection Onur Varol, Clayton A. Davis, Filippo Menczer, and Alessandro Flammini Chapter 13 Feature Generation and Engineering for Software Analytics Xin Xia and David Lo Chapter 14 Feature Engineering for Twitter-Based Applications Sanjaya Wijeratne, Amit Sheth, Shreyansh Bhatt, Lakshika Balasuriya, Hussein S. Al-Olimat, Manas Gaur, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan Index

multidimensional particle swarm optimization for machine learning and pattern recognition

Understanding Machine Learning - From Theory to Algorithms这本书的中文扫描版

Apress出版, 2019年的书。全英文。我还没看，无法发表意见。请自己到Amazon看介绍.

Computers have gained a cardinal place in modern societies, thanks to higher efficiencies and miniaturisation. However, their dramatic progress will soon have to stop as the limits of miniaturisation are being reached. Furthermore, few people realise that those computers are, in fact, not as powerful as they seem to be. And while the world champion at Go lost to a computer, an average human still beats a computer at relatively easy tasks such as recognising an object in a picture. Artificial intelligence is the key to more versatile computing machines capable of solving such challenging tasks.

《机器视觉算法与应用(双语版)》是一本关于机器视觉算法与应用的中英文对照版教材。而且附带有halcon代码，讲解十分详细！

很好的的深度学习材料，值得与大家分享，讲解非常详细~

xgboost是一种集成学习中非常厉害的算法，在kaggle等比赛中取得了非常好的成绩。资源中有作者陈天奇的论文及ppt详解。

搜索方法合集，优化算法大全，大师之作，本书提供了大量的完整的先进的优化搜索算法，希望能给读者带来帮助和启示