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人工智能资料库:第60辑(20170615)

AllenOR灵感
 AllenOR灵感
发布于 2017/09/10 01:28
字数 529
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1.【博客】Predicting Football Results With Statistical Modelling

简介:

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.

原文链接:https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/


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

简介:


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.

原文链接:https://hackernoon.com/deep-learning-cnns-in-tensorflow-with-gpus-cba6efe0acc2


3.【代码】Kubernetes-GPU-Guide

简介:


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.

原文链接:https://github.com/Langhalsdino/Kubernetes-GPU-Guide


4.【博客】Time Series Anomaly Detection Algorithms

简介:


At Statsbot, we’re constantly reviewing the landscape of anomaly detection approaches and refinishing our models based on this research.
This article is an overview of the most popular anomaly detection algorithms for time series and their pros and cons.
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.

原文链接:https://blog.statsbot.co/time-series-anomaly-detection-algorithms-1cef5519aef2


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

简介:

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.

原文链接:http://www.unofficialgoogledatascience.com/2017/03/attributing-deep-networks-prediction-to.html


本文转载自:http://www.jianshu.com/p/a10bebde39aa

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