Week of May 17

2 minute read

What I did

Deep Learning coursework. This week’s was difficult, mostly because they segmented the code in a way that was pretty unclear. For example, backpropagation was split into three different functions in three different classes. It was frustrating.
Read another few chapters of my stats textbook. These were mostly a review of hypothesis testing and associated terms.
I thought about how I would build a word cloud for my facebook chat data.

I realized that I didn’t understand backpropagation as well as I thought. See what I will do next.
Here are some important definitions to remember:
- Null Hypothesis: \(H_0\), the original hypothesis you’re testing.
- One- vs. Two-tailed Tests: If the null hypothesis has ≠ vs. > or <.
- Significance Level: α, the area of the normal curve outside your confidence interval. Confidence interval is 1 - α. Significance level is also the probability of having a type I error.
- Type I Error: α. False positive. This is when you reject a null hypothesis that is actually true.
- Type II Error: β. False negative. This is when you do not reject a null hypothesis that is actually false
- Test Power: 1 - β. Ability of a test to detect false null hypothesis. On the internal of [0,1], 0 means your H-test is worthless, 1 means it’s really good.
- One sample z-test vs. p-value: For a z-test, you compute the critical values \(Z_\alpha\) or \(Z_{\alpha/2} (one-tail vs two-tailed\) for significance level of α. Then reject the hypothesis if the z-score is outside of this range. For a p-value test, you calculate the probability of observing a value as or more extreme than that observed. If p-value is too small, reject the null hypothesis.

p-value

I think I would benefit greatly from writing an article about how backpropagation works. It would likely have to be quite lengthy though, so I might create a separate post for it. It’s also quite difficult to explain without figures, so it would take me some time to do it well.
If I have time next week, I’d like to create a word cloud from my chat data. It shouldn’t take more than a few hours. Most the code is already written. It’s just a matter of properly preprocessing. TF-IDF wouldn’t really make sense unless I fed the program my entire corpus of fb messages. Instead, I think I’ll just need to filter out the most common, useless words by importing a package like STOPWORDS.