hello. I just joined and am using Google Colab. I’m finding that I’m able to use the current notebooks out of the box just fine with the exception of state getting lost whenever I close the notebook, meaning that I have to re-download the datasets to presumably some ephemeral drive. I was wondering if there’s any advice around this.
Also, I noticed that at least the first notebook seems to be completed with all the “answers” and I was wondering if there is any advice around using them in their current state. Thank you!
Hello,
I am doing a research project on post-disaster management.
I am not able to find audio data related to post-disaster management.
Can someone pls guide me where I can find audio data related to the disaster in any sense.
hey there!
I am glad to join the Fast.ai community, looking forward to a lot exciting discussions.
i was going through the online course and im having trouble setting up the fastai on my machine.
my machine configs:
OS: Mac OSX - 10.15.3
installer - pip and pip3
Have you tried creating a virtual environment first with python 3? Doing that seemed to get me sorted out. I, personally, been primarily using colaboratory though.
Hi guys I am new here, and I must confess I am a bit lost with everything going on. I have been going through different threads already but I still have a couple of questions.
A bit of a background I have been using gcloud for my virtual machine on a mac os. I wanted to know
I have been using jupyter through the instance, but I keep seeing that we can use the fastai repo for everything. I do not understand what would be the advantage or use of using the fastai through github rather than cloning a jupyter notebook and doing everything from there instead?
I am never able to get the conda command on the terminal. sudo /opt/anaconda3/bin/conda install -c fastai fastai conda install -c pytorch -c fastai fastai
Both these commands yield: conda: command not found
Thank you, everyone
I managed to get everything working, so I decided to try the steps out on a small dataset. Currently I’m looking at about four categories of ~100-200 images each.
I went through once and had an error of approximately 50%. No big deal. I didn’t bother with any kind of image cleaning or anything.
After cleaning I’m down to around 30% error. So clearly it had an affect, but I’m missing some other kind of secret sauce.
My question is more of one of scale. I plan to go through and clean the images out again, but is it a case where I would just need more images? I figured that my dataset was pretty dang small, but I kind of want to have an idea of how many I should be aiming for with resnet34 and resnet50. Are we looking at 10^3? 10^4? Higher than that? Do we get diminishing returns after a certain point?
Hello! I’m new to this course, I’ve just started and watched the first lesson and I can say… I’m extremely lost, I feel like most of it goes over my head and I wonder why. My first thought is that I haven’t worked with python before and my programming background is hard to determine -I’ve done some little things but probably not the level necessary. So my question is should I try going through the lesson once more or should I first look through some python code and exercises?
If you have very little programming experience, you may need to learn python at the the same time. I have found the strategy of doing the steps in the 2nd link the most effective for me.
I also have to practice what I learn by trying to build something using the lesson material to keep it fresh in my mind.
Keep a python reference that you find really easy to understand close by. I have found the best reference is not what others say is the best, but one you can most easily and quicklyunderstand and use to achieve your goals sometimes its books, tutorials or this forum, or other websites.
Thanks a lot again!
This was of great help. I feel like I know what I’m doing a little bit more now!
I’m still not sure if this is the part of AI I want to pursue, but I will give it a try, the experience look valuable.
The firt is confusing, but in regards to the -nd: have you tried activating anaconda env first?
Usually source ~/anaconda3/bin/activate than call on conda
You can go with conda config --set auto_activate_base True for convenience
In my experience, playing around and seeing what works s great. Also, make sure you follow the course’s tips on fine tuning, like the right learning rate and epoch, as well as looking for the steepest part of the recorder plot.