Another look at Julia
The good news is that Julia has much improved over the years, not only by being more complete (in particular in terms of libraries), but also through changes in the language itself. More changes are about to happen with version~0.4 which is currently under development. The changes being discussed include the array behavior that I criticized three years ago. It’s good to see references to APL in this discussion. I still believe that when it comes to arrays, APL and its successors are an excellent reference. It’s also good to see that the Julia developers take the time to improve their language, rather than rushing towards a 1.0 release.

Speeding up R packages’ installation process
Building R packages from sources may take a long time, especially if they contain a lot of C/C++/Fortran code. Long compile time might be especially frustrating if you are a package developer and you need to recompile your project very often. Here is how long it takes to compile the stringi package on my laptop

ICML 2015 and Machine Learning Research at Google
• Learning Program Embeddings to Propagate Feedback on Student Code
• BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
• An Empirical Exploration of Recurrent Network Architectures
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• DRAW: A Recurrent Neural Network For Image Generation
• Variational Inference with Normalizing Flows
• Structural Maxent Models
• Weight Uncertainty in Neural Network
• MADE: Masked Autoencoder for Distribution Estimation
• Fictitious Self-Play in Extensive-Form Games
• Universal Value Function Approximators
• Extreme Classification: Learning with a Very Large Number of Labels
• Machine Learning for Education
• Workshop on Machine Learning Open Source Software 2015: Open Ecosystems
• Machine Learning for Music Recommendation
• Large-Scale Kernel Learning: Challenges and New Opportunities
• European Workshop on Reinforcement Learning (EWRL)
• Workshop on Deep Learning

List of Data Science Resources
• Getting Started
• Online Courses
• Sites
• Podcasts
• Books
• Python Libraries