10/12-10/23
The last two weeks in the OpenAI Scholars program have been really great. I've met a lot of really cool people and have been learning a ton. I decided that to start out, I wanted to get experience with using pytorch to implement various things, roughly from scratch. The fast.ai course of 2019 (especially part two) was really helpful for showing me how to do this, but doing it myself has been really useful in drilling in the knowledge of how things work. Along the way, I've learned a few interesting things. Standard deviation is biased for small sample sizes. The intuitive explanation for this is that it's calculated by subtracting the mean from your samples, squaring all the values, then summing the squares. When you take the mean of your samples, it's likely to be closer to your samples than the true mean (since it's an average of your samples). Thus, the differences are likely to be smaller, and your standard deviation will be an underestimate. One interesting poi