No single Automatic Differentiation (AD) system is the optimal choice for all problems. This means informed selection of an AD system and combinations can be a problem-specific variable that can greatly impact performance. In the Julia programming languag... (more…)

If you have a large collection of data and have to do similar computations on each element, data parallelism is an easy way to speedup computation using multiple CPUs and machines as well as GPU(s). While this is not the only kind of parallelism, it cover... (more…)

A mixed experience with Julia and saving data frames... (more…)

Julia is able to run very well on you Ipython notebook Environment. After all, All you have to do is Data-Science and Machine-Learning. :) 2. DataFrames: Whenever you have to read lot of files in… (more…)

Tutorials on Julia topics. Contribute to johnmyleswhite/julia_tutorials development by creating an account on GitHub. (more…)