One paper that, in my opinion, will be more influential than the garbage constantly published on many paid journals, is “Train faster, generalize better: Stability of stochastic gradient descent”, written by Moritz Hardt at Google. The authors published it on Arxiv, … Continue reading
Many data scientists are familiar with the tools to analyse large or small datasets. Not so many know what to use when. Here is a summary of the methods that are more appropriate in specific situations. Feel free to download … Continue reading
Extracting knowledge from large datasets with large number of variables is always tricky. Dimensionality reduction helps in analyzing high dimensional data, still maintaining most of the information hidden behind complexity. Here are some methods that you must try before further … Continue reading
Extracting knowledge from large datasets with large number of variables is always tricky. Dimensionality reduction helps in analyzing high dimensional data, still maintaining most of the information hidden behind complexity. Here are some methods that you must try before … Continue reading
Disclaimer This article should be read with the sole purpose of education. No users nor machines must be hurt or damaged. Ethics first. Fun later. Modern x86 Operating Systems, use protected mode to execute instructions. In protected mode there are … Continue reading
