Today I learned way more about quotations in bash than I ever thought I needed to know. I thought I would highlight the interesting use case that I discovered, which requires some special trickery to write a script that executes arbitrary commands. First, let's quickly review some facts about bash quotes.
Last month, my class decided that we should get a fridge for the class study room. This brought up an important question: how would the fridge be cleaned? I thought this was an interesting problem and deserved some discussion, both from a practical and a theoretical standpoint.
One dream I have always had since I started learning languages is to be able to go to another country and use that language to communicate. This August I had the first opportunity to do that during a 2 week trip to Japan. In this post, I will outline the preparation I did before going, where I was able to use it when I was there, and evaluate my success.
Over the course of my internship at the online shopping company Wish, I have dealt a lot with a lot of data in the form of sparse matrices, specificaly in the form of item interaction matrices for customer data. In doing so, I have made heavy use of scipy's sparse matrices library. Here are 5 tricks that I have learned.
People are notoriously bad at estimating their percent confidence in their beliefs, as explained further in this Wikipedia article. Something I thought of recently is what effect this overconfidence has from a Bayesian perspective. After a bit of math, I came to the conclusion that having extreme confidence in your beliefs (0% or 100% confidence) implies that you would be unable to change your beliefs if shown evidence to the contrary. I believe this simple argument suggests that it is very irrational to hold prior beliefs of 0 or 100%. If you do feel this way, then you should choose a very high value (99.99%) or a very low value (0.001%), but always leave some room for error.
Everybody doing machine learning wants the best models possible. The aim of this blog article is the following:
This strange question came up when working on a machine learning project to generate embeddings. Working with the version of Pytorch available on our DGX (similar to version 0.3.1), I found there was an optimizer called SparseAdam but not one called SparseSGD. Since what I really wanted to do was use SGD, I wondered: could I turn the Adam optimizer into an SGD optimizer by setting the hyperparameters \(\beta_1\), \(\beta_2\), and \(\epsilon\)?
Ever since I've started doing internships, the concept of efficient organizations has piqued my interest. In every workplace I have been in, time is always wasted by inefficient transfers of information. For example, long meetings where most of the content is irrelevant to most of the people, or repeated interactions with co-workers where you explain the same thing to all of them. Assuming employees make ~$40/hour, a 1 h meeting with 100 people will cost $4000! If these meetings are not productive, then the company gets a negative return on its time investment, which essentially means the company is wasting money. Clearly organizational efficiency is a financially important objective.
Much of an organization's efficiency can be linked to its structure. Long, big meetings are usually a consequence of a strong hierarchical organization, where work is done by employees, then synced to a centralized node (a boss), and then possibly recapitulated to the workers in a meeting. However, are there better ways to structure an organization that would save time?
Lately I have been putting a lot of effort into studying Japanese, to prepare for my upcoming trip to Japan at the end of summer 2018. While learning the readings for various kanji (Chinese character) words, I've noticed that a lot of their pronunciations (on-yomi) are related to the Chinese pronunciations in an interesting way. Let me explain my empirical theory about how to to convert Chinese pronunciations into Japanese ones:
Didn't know that creating a decent looking website could be this easy. Thanks Jekyll. Also thanks to this website for walking me through the process.