Meeting date: 6/12/2018
Firstly, I was great to see you all at The Tech Hive!
Watch lesson 2 video.
Although there is no specific homework mentioned by Jeremy in Lesson 2 video, there is some clarification on what's suggested at fastai formus.. See also below:
-
Notebooks: lesson1.ipynb
-
Suggested work: follow this approach with the above notebook, and then do same steps on another dataset of your choice.
Extract from above link:
How to use the Provided Notebooks
- Read through the notebook. If everything makes sense, put it aside and create a new notebook.
- Now try to code the same process as we went through in class.
- If you get stuck at any point, you can refer to the class notebook. Find the solution to what you are stuck on. Look up the relevant documentation. Put the class notebook aside again, go back to your notebook, and try to code the solution.
- If you are still stuck, you can refer to the class notebook again. Do not copy and paste the needed code. Instead, type it out yourself. Check that it runs. If so, try changing the inputs, and see if that effects the outputs as you expect.
- Any time that you feel unsure about why a particular step is being done, or how it works, or why the outputs and inputs are what you observe (or anything else!), please ask on the forums. As I write this (week 3 of the course) there has not been a single question on the forums that has not been resolved! :)
If the above process is easy for you, you can re-create the class notebooks with a different dataset (Look at Image Datasets for ideas).
Lesson 2 session is on June 19th at Tech Hive at 6:30pm. Watch out for meetup announcement to register. See you there!
We will use #deep_learning channel at Cleveland Tech on Slack. Join Cleveland Tech on Slack with this link then join #deep_learning channel.
Fast.ai Wiki: Lesson 2. Fastai forums is great resource. I encourage you to use fastai forums!