Your Mission (Continued)
~1 Hour 20 Minutes
Build on our work from class to formalize your play from the last level. Once you've merged the data from Dewey, Cheetham, and Howe, created some potentially useful features, and turned everything into numbers, upload a csv of your data to GitHub with the name dewey-cheetham-howe.csv. See
FWIW, preparing instructional material is an exercise in compression, and it's not lossless. The hope is that you now have a very high-level sense of how things work. For example, I glossed over how the activation function behaves with regard to the word2vec "hidden"/projection layer. Spoiler: it's not a sigmoid! Actually, there is no activation function. We just pass on the weights. That being said, I didn't explain weights very deeply. So again, as it says in the title—oversimplification. ;)
If you want to learn more about some of the topics discussed in the video above, and you have some free time, you might enjoy the following.
~ 1 Hour and 10 Minutes
Training Your Algos: Linear Regressions
Note: none of your missions will ask you to perform a linear regression. So don't worry too much about the details discussed above. Given how much we've talked about regressions, I figured we should build at least one together as a class.
We are working with the notebook file training.ipynb (pre-loaded for those of you using Pythonanywhere).
Optional: If you want to learn more about some of the topics discussed in the video above, and you have some free time, you might enjoy the following: Guess the Correlation (a video game where you guess the R-squared).
Training Your Algos: Binary Classifiers
Again, we are working with the notebook named training.ipynb (pre-loaded for those of you using Pythonanywhere).
Optional: If you want to learn more about some of the topics discussed in the video above, and you have some free time, you might enjoy the following.
Your Mission (Continued)
Take the data you prepared above (i.e.,
https://[your username].github.io/ctl/dewey-cheetham-howe.csv) and use it to train the classifiers found in training.ipynb under the Classifiers Section. Remember I've only loaded libraries for the Python 3.5 kernel. So if you make a new notebook, be sure it's using 3.5.
Stretch Goal: Add a new classification algo from scikit-learn to your notebook. Take a screen shot of the evaluation screen for this algo, and upload it to GitHub with the name new_algo. E.g.,
Note you should follow the pattern of the algos shown above. That is, you can just copy one of the example cells, swap out the first two lines and edit variable names accordingly. You might find some inspiration here.
Your Final Project
Enrolled students will be presenting on their final project in one week. Take whatever time you have left to work on your project, even if it's just planning or skills acquisition (e.g., working through the optional docassemble training from level 3). See The Final Project Rubric.
Self-Reflection and Logging Your Work
As we do at the end of every level, we ask that you take a few minutes to reflect on how things are going. I've also included a set of reading questions to queue things up for our synchronous discussion. Your answers will be shared with me and it will let me know that I can look for any project work you may have posted. That being said, you've almost completed Level 7. Tell me how it's going by completing the form linked below.
† Time estimates are just that—estimates. The assumptions used to calculate reading time are as follows: 48 pages is assumed to take roughly an hour to read. When working with non paginated texts, it is assumed that a page is roughly equal to 250 words. Videos assume both 2X and 1X viewing. Estimates for coding are based on past experience. Each level should include about 6 hours and 40 min of work.
Synchronous Meet Up, AKA our Class Time
1 Hour and 30 Minutes | October 13, 2020 @ 4pm Eastern
If you're an enrolled student, we'll be meeting at this link on TUESDAY October 13th at 4pm via Zoom. If you don't have the password, and you are a registered student, DM me on Slack, and I can give you the password. If you're not an enrolled student, I'm afraid you can't join us.
We will use this time to: (1) troubleshoot any issues folks might have had working through the your mission; and (2) discuss the readings.