## 人工智能资料库：第60辑（20170615） 转

AllenOR灵感

1.【博客】Predicting Football Results With Statistical Modelling

Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League”. You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today”. But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here). As we’ll discover, a simple Poisson model is, well, overly simplistic. But it’s a good starting point and a nice intuitive way to learn about statistical modelling. So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house.

2.【博客】Deep Learning CNN’s in Tensorflow with GPUs

In this tutorial, you’ll learn the architecture of a convolutional neural network (CNN), how to create a CNN in Tensorflow, and provide predictions on labels of images. Finally, you’ll learn how to run the model on a GPU so you can spend your time creating better models, not waiting for them to converge.

3.【代码】Kubernetes-GPU-Guide

This guide should help fellow researchers and hobbyists to easily automate and accelerate there deep leaning training with their own Kubernetes GPU cluster.
Therefore I will explain how to easily set up a GPU cluster on multiple Ubuntu 16.04 bare metal servers and provide some useful scripts and .yaml files that do the entire setup for you.

By the way: If you need a Kubernetes GPU-cluster for other reasons, this guide might be helpful to you as well.

4.【博客】Time Series Anomaly Detection Algorithms

At Statsbot, we’re constantly reviewing the landscape of anomaly detection approaches and refinishing our models based on this research.
This post is dedicated to non-experienced readers who just want to get a sense of the current state of anomaly detection techniques. Not wanting to scare you with mathematical models, we hid all the math under referral links.

5.【博客】Attributing a deep network’s prediction to its input features

Deep networks have had remarkable success in variety of tasks. For instance, they identify objects in images, perform language translation, enable web search — all with surprising accuracy. Can we improve our understanding of these methods? Deep networks are the latest instrument in our large toolbox of modeling techniques, and it is natural to wonder about their limits and capabilities. Based on our paper [4], this post is motivated primarily by intellectual curiosity.

### AllenOR灵感

【Technews科技新报】预测时尚潮流是一项需要天分的工作，还得仰赖一个庞大的系统让少数人追捧的时尚进入大众流行市场，进而让业者赚取大笔钞票。现在预测工作也可以交给人工智能，让服饰业者...

2017/12/26
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1.【会议】Bayesian Deep Learning 简介： While deep learning has been revolutionary for machine learning, most modern deep learning models cannot represent their uncertainty nor......

chen_h
2017/12/03
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2017/06/03
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【概要】2018年9月7日，美国国防高级研究计划局（DARPA）宣布将投资20亿美元研发下一代人工智能（AI）技术。由于目前主流AI技术依赖于大量高质量的训练数据，很难适应不断变化的外部条件，有...

09/19
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BAT机器学习面试题及解析（281-285题）

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2017/12/20
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1、开发者和架构师之间最大的区别是什么？ 架构师和开发者一样，也经常写代码，简单的说，开发者和架构师之间最大的区别就是技术领导力。 软件架构师的角色需要理解最重要的架构驱动力是什么...

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https://stackoverflow.com/questions/15423500/nginx-showing-blank-php-pages For reference, I am attaching my location block for catching files with the .php extension: location ~......

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