Hey guys, I had done part 1 of the course a few months back but did not give in the required homework time, but I did get the concepts. Now I have 1.5 months free and want to do both parts seriously. I was thinking maybe I should do Part 2 first and then revisit part 1. What this be ok or does Part 2 use material from part 1 ? I was thinking the code would make more sense.
F.Y.I: I am working as a deep learning researcher for a year so I am very familiar with DL concepts in general.
Since you worked as a deep learning researcher for a year, my suggestion is to do Part 2,and then go back to Part 1 if necessary. Part 2 covers building a deep learning library from scratch. This will make you more effective researcher in my opinion. It will teach you to write better code which will increase your productivity.
Hi, Part1 is going to be quite different with Fastai v2 so I suggest you not to rewatch it.
Besides, being a DL researcher is not easy nowadays: the field is evolving quite fast, and you have to read new papers every week: time is precious!! If you have a good understanding of the basic concepts like cnn, rnn, traningWithTricks then go for Part2, do what Jeremy suggests to do. Once you complete the course pick a paper you like and experiment with it. Then keep studying hard!!
I am taking about part 1 v3 itself
Are the part 1 concepts not needed in part2?
yes I know. since you haven’t taken part 2 yet, you do not probably know that fastai (library) is under a major revision towards Fastai v2.0
Hi @bluesky I think that jumping into Part 2 is ideal for someone who is, like yourself, already a deep learning practitioner. There’s a Part 2 v3 Study Group sponsored by TWiML; we meet every Saturday at 8:45 Pacific Time via Zoom. In between meetings we use the TWiML & AI Meetup Slack group, channel #fast_ai_dl We are starting Lesson 10 this Saturday. You are welcome to join us! Sign up for the meeting reminders (containing the Zoom link) and get a Slack invitation at: https://twimlai.com/twiml-x-fast-ai/
You can use this Jupyter notebook to set up to run the course notebooks on Google Colab.
Thanks! I will try to catch up. Is there a recorded or text version of previous meet ups?
Recorded versions of the previous meetups can be found on the #fast_ai_dl channel of the TWiML Slack group. I’ve also shared annotated versions of the course notebooks
03_minibatch. I’d focus on reading and running and understanding these notebooks rather than the recorded meetups.
To catch up:
- Set up (either locally or on the cloud) your infrastructure with the capability to run the course notebooks. To run on Google Colab, use the Jupyter notebook I shared above.
- Listen to Jeremy’s Lesson 8 and Lesson 9 lectures
- Review run, and play with course notebooks 00, 01, 02, and 03. I will review notebooks
05_anneal.ipynb this Saturday, 8/10/2019.
Don’t worry if you can’t accomplish all of this by Saturday; I know it’s a lot. Just do as much as you can. You’ll get there. See you Saturday!
Part 2.1 proved to me I had not really understood Part 1 so I did Part 1 5 and 6 again and then I did Part 2.2. As a novice it is difficult to remember the detail and at the same time I am still learning Python. I really like Part 2 because I like to understand how things work. I think Jeremy did state you should look back at the later sections.