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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 and to do the calculations and to interactively visualize the results.
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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]
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3.【博客】Building a Chatbot using Telegram and Python
简介：
Chatbots are all the rage at the moment, with 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 , 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 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 .
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.
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4.【资料】Tutorial Slides by Andrew Moore
简介：
各种机器学习的PDF。
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5.【课程】MIT Machine Learning for Big Data and Text Processing Class Notes
简介：
As a follow up on , I am currently in Boston attending (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.
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转载地址：http://kvdqb.baihongyu.com/