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Building Ontologies with Basic Formal Ontology 如何建立本体论知识图谱

斯坦佛大学Danqi Chen的毕业论文。非常优秀的关于神经网络应用在自然语言理解的综述论文。

疯狂python讲义 李刚 2018年版 疯狂系列出品 都是精品

A Beginner’s Guide to HTML, CSS, JavaScript, and Web Graphics 基本的网页开发技术和原理

All of Statistics A Concise Course in Statistical Inference 统计推断经典英文教程 数据科学和机器学习必备基础

本资料来自udacity，关于数据科学入门的职业指南。 1. Introduction 2. Career Options: Analysts, Scientists, and Engineers 3. In-Demand Skills: The Sexiest Job of the 21st Century 4. Salaries: What do Data Analysts make, and where do they make it? 5. Roles and Skills: What you need to know to get hired 6. Soft Skills: Qualities you’ll need to succeed 7. The Interview: Strategies to nail it 8. Your Portfolio: Your secret weapon for getting hired 9. Your First Data Job: What it will be, and how to get it 10. The Future of Data: A Look at Predictive Analytics 11. Conclusion Introduction

A new lightweight, modular, and scalable deep learning framework Training and deploying AI models is often associated with massive data centers or super computers, with good reason. The ability to continually process, create, and improve models from all kinds of information: images, video, text, and voice, at massive scale, is no small computing feat. Deploying these models on mobile devices so they’re fast and lightweight can be equally daunting. Overcoming these challenges requires, a robust, flexible, and portable deep learning framework. We’ve built Caffe2 with this goal in mind. Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI on mobile devices. This release provides access to many of the same tools, allowing you to run large-scale distributed training scenarios and build machine learning applications for mobile.