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人工智能资料库:第21辑(20170130)

AllenOR灵感
 AllenOR灵感
发布于 2017/09/10 01:26
字数 606
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  1. 【资料】Finding Similar Music using Matrix Factorization

简介:

One interesting technique I didn't cover was using Matrix Factorization methods to reduce the dimensionality of the data before calculating the related artists. This kind of analysis can generate matches that are impossible to find with the techniques in my original post.

This post is a step by step guide on how to calculate related artists using a couple of different matrix factorization algorithms. The code is written in Python using Pandas and SciPy to do the calculations and D3.js to interactively visualize the results.

原文链接:http://www.benfrederickson.com/matrix-factorization/


2.【博客】Dissecting Reinforcement Learning-Part.1 & Part. 2

简介:

Premise[This post is an introduction to reinforcement learning and it is meant to be the starting point for a reader who already has some machine learning background and is confident with a little bit of math and Python. When I study a new algorithm I always want to understand the underlying mechanisms. In this sense it is always useful to implement the algorithm from scratch using a programming language. I followed this approach in this post which can be long to read but worthy. I am not a native English speaker so if you spot some mistakes that make a sentence puzzling please report it in the comments below and I will fix it]

原文链接:https://mpatacchiola.github.io/blog/2016/12/09/dissecting-reinforcement-learning.html

原文链接:https://mpatacchiola.github.io/blog/2017/01/15/dissecting-reinforcement-learning-2.html


3.【博客】Building a Chatbot using Telegram and Python

简介:

Chatbots are all the rage at the moment, with some predicting that they will be bigger than mobile apps. The main idea of chatbots is that instead of having to dig through awkward mobile menus and learn UIs, you'll simply have a conversation with a bot through a familiar instant messaging interface. If you want to order a Pizza, you start a conversation with the Domino's Pizza bot and have the same conversation with it that you might have with a human.

There are a few different platforms that allow you to build your own chatbot. One of these, which is arguably the simplest to use and is also growing steadily in popularity, is Telegram.

In this tutorial, we'll walk through building a simple Telegram Bot using Python. At first, our bot will simply echo back any message we send it, but then we'll extend it to add a database and persist information across chat sessions.

原文链接:https://www.codementor.io/garethdwyer/building-a-telegram-bot-using-python-part-1-goi5fncay


4.【资料】Tutorial Slides by Andrew Moore

简介:

各种机器学习的PDF。

原文链接:https://www.autonlab.org/tutorials


5.【课程】MIT Machine Learning for Big Data and Text Processing Class Notes

简介:

As a follow up on MIT's tackling the challenges of Big Data, I am currently in Boston attending Machine Learning for Big Data and Text Processing Classification (and therefore blogging about it for posterity based on public domain data / papers - nothing posted here is MIT proprietary info to violate any T&C). MIT professional education courses are tailored towards professionals and it is always a great opportunity to learn what others practitioners are up to, especially in a relatively new field of data science.

原文链接:

http://blog.adnanmasood.com/2015/06/08/mit-machine-learning-for-big-data-and-text-processing-class-notes-day-1/

http://blog.adnanmasood.com/2015/06/09/mit-machine-learning-for-big-data-and-text-processing-class-notes-day-2/

http://blog.adnanmasood.com/2015/06/11/mit-machine-learning-for-big-data-and-text-processing-class-notes-day-3/

http://blog.adnanmasood.com/2015/06/12/mit-machine-learning-for-big-data-and-text-processing-class-notes-day-4/

http://blog.adnanmasood.com/2015/06/12/mit-machine-learning-for-big-data-and-text-processing-class-notes-day-5/


本文转载自:http://www.jianshu.com/p/f1e81608a857

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