Top /r/MachineLearning Posts, Apr 1218: Andrew Ng AMA, Autoencoders, and Deep Learning Textbooks
Tags: AirBnB, Andrew Ng, Baidu, Deep Learning, Grant Marshall, Open Source, Reddit, Sentiment Analysis, Textbook
Andrew Ng's AMA, a probabilistic view of Autoencoders, open source sentiment analysis, deep learning textbooks, and Airbnb's host matching are all discussed this week on /r/MachineLearning.
Grant Marshall
This week on /r/MachineLearning, we have Andrew Ng’s AMA, an autoencoders paper, open source comparisons, deep learning textbook recommendations, and Airbnb’s use of machine learning.
1. AMA Andrew Ng and Adam Coates +384
This AMA (which has been scheduled for a few weeks) includes Andrew Ng and his Adam Coates from Baidu Research. In it, there is much discussion on topics like MOOCs and education, largely because of Dr. Ng’s experience with his very popular Coursera ML MOOC. Certainly give this a read if you’re interested in education or what Baidu Research is working on.
2. A new Favourite Machine Learning Paper: Autoencoders as Probabilistic Models +77
This post is a review of a paper on Autoencoders and probabilistic models. In it, the author details how to model a problem using denoising autoencoders. In particular, the author discusses the probabilistic understanding of these autoencoders. If you’re interested in deep learning or probability, give this a shot.
3. A comparison of open source tools for sentiment analysis +55
This blog post is a survey of sentiment analysis methods using open source tools on the Yelp Dataset. It details the relative performance of naïve bayes using unigrams/bigrams, stopwords, and WordNet in the classifier. This is a nice practical introduction to making a sentiment analysis engine.
4. What is the best Machine Learning Textbook out there, which has good directions into Deep Learning? +54
This self post seems to be very popular, considering the relative newness of the deep learning buzz. The most popular choice is Kevin Murphy’s “Machine Learning: A Probabilistic Perspective” (which I personally like) with Bishop’s text also mentioned by a few. Another option to consider, if this is something you’re looking for, is the free online Neural Networks and Deep Learning, which is a bit newer than the other two.
5. How Airbnb uses machine learning to detect host preferences +52
This blog post is a sort of case study into how Airbnb built their host preference prediction engine. In the post, the author details why they chose to build their own logistic regression model instead of adopting a collaborative filtering approach. If you’re interested in these types of consumerfacing models and how they are developed, this is an informative post.
Related:
This week on /r/MachineLearning, we have Andrew Ng’s AMA, an autoencoders paper, open source comparisons, deep learning textbook recommendations, and Airbnb’s use of machine learning.
1. AMA Andrew Ng and Adam Coates +384
This AMA (which has been scheduled for a few weeks) includes Andrew Ng and his Adam Coates from Baidu Research. In it, there is much discussion on topics like MOOCs and education, largely because of Dr. Ng’s experience with his very popular Coursera ML MOOC. Certainly give this a read if you’re interested in education or what Baidu Research is working on.
2. A new Favourite Machine Learning Paper: Autoencoders as Probabilistic Models +77
This post is a review of a paper on Autoencoders and probabilistic models. In it, the author details how to model a problem using denoising autoencoders. In particular, the author discusses the probabilistic understanding of these autoencoders. If you’re interested in deep learning or probability, give this a shot.
3. A comparison of open source tools for sentiment analysis +55
This blog post is a survey of sentiment analysis methods using open source tools on the Yelp Dataset. It details the relative performance of naïve bayes using unigrams/bigrams, stopwords, and WordNet in the classifier. This is a nice practical introduction to making a sentiment analysis engine.
4. What is the best Machine Learning Textbook out there, which has good directions into Deep Learning? +54
This self post seems to be very popular, considering the relative newness of the deep learning buzz. The most popular choice is Kevin Murphy’s “Machine Learning: A Probabilistic Perspective” (which I personally like) with Bishop’s text also mentioned by a few. Another option to consider, if this is something you’re looking for, is the free online Neural Networks and Deep Learning, which is a bit newer than the other two.
5. How Airbnb uses machine learning to detect host preferences +52
This blog post is a sort of case study into how Airbnb built their host preference prediction engine. In the post, the author details why they chose to build their own logistic regression model instead of adopting a collaborative filtering approach. If you’re interested in these types of consumerfacing models and how they are developed, this is an informative post.
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