My commute to work is a 3-hour journey every day, with 1 hour on the train and half an hour on foot each way. Friends who visit me from the city always ask, “Do you commute like this every day? Aren’t you exhausted?” I thought this way too before moving out from the city, where I walked for only 10 minutes between home and school. But now, I am glad that I have 3 full hours of uninterrupted solo time every day, and the switch of environment between the fast-paced urban and the serene suburban life helps refresh my brain and reset my mind.
I prepare small “learning bento” before going to work, and spend the time on the train either watching MOOC (such as Coursera) and YouTube videos, or reading books and articles, and spend the time on foot listening to podcast or Audible. 3 hours a day adds up to 15 hours a week and 60 hours a month. And over the past few months, I have discovered a lot of great learning resources and would like to share with those who also like to have some “learning bento” for the mind while commuting on the grind.
I started to use YouTube for learning this year following a friend’s suggestion and was astonished that there are tons of great tutorials and playlists with vivid animation and clear explanation. Here are a few channels that I subscribed to.
- Ben Lambert
- Great explanation of undergraduate & graduate courses in econometrics, key concepts in statistics and deep understanding of linear regression, time series prediction, etc, with great handwriting and examples.
- Victor Lavrenko
- Clearly explained machine learning algorithms and information retrieval.
- Extremely awesome visualization of linear algebra and neural net.
- StatQuest with Josh Starmer
- Clearly explained fundamental statistics theory and models.
- Lex Fridman
- Interesting interviews and guest talk from industry leaders in artificial intelligence.
Coursera App allows you to download videos and watch them offline. I enrolled in one Coursera specification in the past few months: Data Mining, which covers text retrieval, search engine, pattern discovery, and recommender system.
There is only 1 book on statistics that I have been reading in the past few months.
- Elements of Statistical Learning https://web.stanford.edu/~hastie/Papers/ESLII.pdf
- Absolutely a must-read for any data scientist and machine learning engineer. It has a lot of equations and formula which are not easy to understand by self study. I usually watch some YouTube videos and read more intuitive explanation in order to have a good understanding of the book.
To keep up with recent research development in the field and to understand certain specific research topics related to my project, I read research articles on a regular basis. My recent reading focuses on time series analysis and featurization, and word embeddings.
Social media and newsletter
I follow active members (both company or individual) in my LinkedIn network and they often post interesting development and news in machine learning, such as Towards Data Science https://towardsdatascience.com/
I subscribe to the newsletter Data Science Weekly https://www.datascienceweekly.org/ and they send weekly newsletter about recent blogs, articles, and resources.
And of course, the pleasure of binge watching my favorite genre on Netflix: Sci-fi. Shout out to the Black Mirror fans!