GSFRS(Giant Signal File Random Sampler)

S peeding up Model Training with Multithreading and GSFRS                   written by Rahat Ahmed Talukder , Notre Dame University Bangladesh                  We live in a multicore universe where great things can happen in parallel. Parallel processing is equivalent to enormous performance gain. Organized parallelism is how our own body works through dynamic bit organized activation of billions of single neurons. Everybody wants to parallelize a workload done on a data frame. In the machine learning (ML) lifecycle, different workloads are parallelized across a large VM. This allows you to take advantage of the efficiency of the VM and maximize the use of your notebook session. Nonetheless, many of the machine learning or scientific libraries used by data scientists ( Numpy, Pandas, sci-kit-learn,...) release the GIL, allowing their use on multiple threads. It is important to keep in mind that when our dataset is large, threads are more practical than processes because of the possible

  Catalyzing A Data Science Revolution: SOCKS + GSFRS Written by Gitika Gorthi, Chantilly High School Why the technologies Giant Signal File Random Sampler (GSFRS) and Statistical Outlier Curation Kernel Software (SOCKS) ? How will they benefit you in achieving your data science goals? “Data is the new fuel of the digital economy” or can be viewed as the new gold; harnessing and accurately decoding the meaning of the numbers is crucial to increase two types of efficiency for organizations: speed and accuracy. GSFRS addresses speed and SOCKS addresses accuracy, coupled together, make the power team. Suppose you are a data analyst and you are assigned to take a bunch of numbers and make sense of them -- I know your reaction, you are most probably scared. But don’t worry, we have artificial intelligence to the rescue in order to develop algorithms to do the hardwork for us (yay!). Now the question is, is the program really telling us the right information? Some may trust blindly whatever