This article explains the basics of some Artificial Neural Network models an overview of the recent history Artificial Neural Networks, from MLPs to GANs. Feel free to join the discussion!
Huray! The Data Blogger blog is enlisted in this top 75 of Data Science blogs. This is a good moment to give an overview of some of the most influential blogs for Data Science.
Data Science Central has multiple authors. Besides blog posts they also provide video material. You can also find job postings here. In my opinion they mainly focus on practical stuff and on discussion and they focus less on theoretical posts.
Just like Data Science Central, DataTau collects blog posts from multiple authors. It provides an RSS feed of the most influencial blog posts. Also here is a lack of theoretical posts, but many practical posts and links to sources can be found here.
One of my personal favorites! This blog does provide sufficient theoretical posts and the topics are quite diverse here. This is one of my inspirational sources.
This is it for now. For more interesting Data Science blog you should definitely take a look at Feedspot. I will be back in a few weeks and start writing posts again. As for now, please fill in the poll in the menu, since the next post will be based on that.
End 2013: I would like to lose some weight. End 2014: Maybe I should start losing some weight. End 2015: I should definitely lose some weight. A few weeks ago: I need to lose weight. At that point and bought a book and read a lot of literature about this topic. I am following a low carbs diet and this resulted into a weight loss of 6 kg in the first 3 weeks! The key points are summarized in this post. My goal is to eventual apply some machine learning algorithms on the data I am currently collecting. If you tried a diet before or if you have collected some related data, please let me know. I will use it for the machine learning post on this topic. In this post I will summarize the key points from the literature and show some visualizations of the results so far.