Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.
- State-of-the-Art AI: Building Tomorrow’s Intelligent Systems
Peter Norvig, Director of Research for Google, on developing state-of-the-art AI solutions for building tomorrow’s intelligent systems.
- Soon We Won’t Program Computers. We’ll Train Them Like Dogs
A Wired article on the cognitive revolution, the end of code, and more.
- The fraudulent claims made by IBM about Watson and AI
Bad coverage for IBM this week regarding what Watson is, and how it is marketed. More discussions here.
- Introducing our Hybrid lda2vec Algorithm
“The goal of lda2vec is to make volumes of text useful to humans (not machines!) while still keeping the model simple to modify. It learns the powerful word representations in word2vec while jointly constructing human-interpretable LDA document representations.”
- Visualizing city similarity
This blog post explains an alternative way to figure out how similar cities are.
- Unit testing in data science
“An interesting topic we often hear data science organizations talk about is “unit testing.” It’s a longstanding best practice for building software, but it’s not quite clear what it really means for quantitative research work — let alone how to implement such a practice.”
- Autoencoding Blade Runner
“In this blog I detail the work I have been doing over the past year in getting artificial neural networks to reconstruct films — by training them to reconstruct individual frames from films, and then getting them to reconstruct every frame in a given film and resequencing it.”
- Feed-forward neural doodle
“What if you could only sketch the picture like a 3-years old and everything else is done by a computer so your sketch looks like a real painting? It will certainly happen in near future. In fact several algorithms that do the thing very well were proposed recently, yet they take at least several minutes to render your masterpiece using a high-end hardware. We make a step towards making such things available for everybody and present an online demo of our fast algorithm.”
- One Chart, Twelve Charting Libraries
“Charting Libraries. Gosh, there are so many out there. On Wikipedia and other websites, one can find a comparison of ca. 50 libraries – and these are only JavaScript libraries; not mentioning languages like Processing and libraries for Python and R. In the following blog post, I will try to get to know a few ones out of the great sea of possibilities. I want to understand their differences and how easy it is to learn them.”
- Comparing Daily Stock Market Returns to a Coin Flip
This post examines the Random Walk Hypothesis as applied to daily stock market returns.
- Algorithm Visualizer
See how various popular algorithms work, straight in your browser.
- Employment, construction, and the cost of San Francisco apartments
Very interesting analysis on the house prices in San Francisco. Also see this post.
- Impatient R
This is a tutorial for beginning to learn the R programming language, geared towards the impatient.
- Easier data analysis in Python with pandas (video series)
New video series on pandas.
- Fizz Buzz in Tensorflow
Hilarious article from Joel Grus.
