I am slowly catching up with the rest of the class. I am still on Lesson 2.
I combined Pankaj Mathur’s very nice web app layout and Simon Willison’sidea of also including the option of adding URLs and did a black/grizzly/teddy finder app.
Together with @r0mer0m we wrote an intuitive guide to understand the BERT architecture (based on the Transformer) which has recently achieved SOTA in various NLP tasks. We appreciate your feedback in how to make it as clear and straightforward as possible.
Hi @pankymathur,
I did all of that before I started and still got the error. The only thing that changed since my log in was my update to Mojave. Thank you for your help!!!
Using ResNet-50, I trained an image classifier and deployed it on Zeit so Terminator can now SEE and detect its 4 greatest threats - Elon Musk, Jeff Bezos, Jon Snow, and John Connor! Humanity’s fate is in the hands of these leaders
To help people visualize the architecture of a CNN and understand some of the “under the hood” math, you can check out these spreadsheets and blog post I wrote:
Big shout out and special thanks to @navjots for helping me with the Zeit deployment. As a professional spreadsheet slinger (work in finance) who knows “just enough” Python to get by, I’m incredibly grateful to @jeremy /@rachel’s mission to make machine learning accessible to people of all backgrounds and this community’s willingness to support each other.
Next steps:
Clean Up + Share - Clean up notebook & share repo on Git Hub (my 1st ever! )
Fine Tune + Experiment - Update weight decay parameter, further learning rate experimentation, etc.
Prune image dataset & automate - Using the Fatkun batch image downloader, I used ~750 images for training (600 for training set, 150 for validation) and got to ~97% accuracy (4 losses below are reasonable…I should have removed the “actor” who played the child John Connor)
Hey everyone! I just completed my first blog post.
It’s a quick intro to classifying audio using image classifiers with the fastai framework. I have been trying to transfer some of my work from the freesound kaggle competition to the updated v1 library.
Over the past week or so I wrote an experimental audio module to read in raw audio and compute spectrograms on the fly during training (straight from the DataLoader), so you don’t need to actually create images beforehand. It then can take pretrained image models, modify them to take a single channel of 2D input, and finetune from there. I was able to run 3 epochs over 100k 4 second audio files from the NSynth dataset, classifying their instrument families to around 80% accuracy in under 3 minutes.
I’m planning to continue working on this for the next few weeks in hopes of supporting data augmentation and maybe even support for finetuning of models trained on audio data (as opposed to imagenet models).
It’s still a work in progress, and ALREADY a version behind fastai (currently tested with 1.0.28). You can view all the code and notebooks here:
Maybe it would make sense to add a relu and an additional linear layer on top, so that instead of just a weighted combination of NLP and a tabular model activations you would also get their interactions?
Also you mention that the same learning rates are used for both models, which sounds like an important limitation. Maybe the learned bottom layers weights should be kept frozen and only the top layers trained?
You wanna try Docker Toolbox for Windows (legacy version) instead of Docker for Windows which supports Windows pro and Windows Enterprise, Try this article.
I love this! This is a great example of how to create your custom ItemBase and ItemList
Just one remark to help you port your code to the latest version of fastai, the show_batch method in AudioItem should now be show_xys in AudioItemList (and you can code show_xyzs if you want show_results to work).
For all the ways you can customize your own ItemList, there is now a tutorial, just in case you didn’t see it before.
That all seems valid and I don’t see an error there. Can you check the console on the browser? Also, are you serving via HTTPS, most browsers require this to access the camera.
According to your code if its non HTTPS then the code is forcing to serve in https. issue is when i run python server.py and try to acess its get loaded for a while without webcam on and suddenly the web-page dies.