I have created Marvel Heroes Classifier. Although it cannot guess much correctly. The model can guess correctly when the photo contains distinct pattern (Shield in Captain America, Arrows and bow in Hawkeye, etc).
I trained for about 6 epoch.
Also created the simple website using api from huggingface. Additionally while guessing it extract related Wikipedia page data.
http://marvelheroes.soepaing.com
Hi there, are you all right?
I have a big interest in AI generated art, so I made a classifier based on the one from Lesson 1 (Is it a bird?) that classifies if a AI generated picture is generated by dalle2 or by midjourney. Here’s the link to the notebook, hope you enjoy!
I’ve deployed a bird species classifier for over 600 species living in France.
It’s got a decent accuracy of about 90%, with most mistakes on species from the same family that can be notoriously hard to distinguish.
I wrote a blog post describing how I approached the task and sharing my thoughts/appreciation for the fastai course/book combo.
I created a classifier to identify the brand of different luxury handbags. It gets to ~85% accuracy when identifying among 5 different brands. Feel free to try it on huggingface!
Fun fact: I first tried to identify sunsets from sunrises, but apparently they are non-distinguishable. The model had only 55% accuracy, barely better than random guessing
Our paper on finding ancient Maya settlements in LiDAR with Deep Learning (and winning the 2nd prize in the #ECMLPKDD European Conference on Machine Learning 2021 Discovery Challenge) is finally online:
Key ingredients: Fast.ai and data augmentation via synthetic data.
New Zealand bird classifier now on Hugging Face. Yay!
Also discovered someone beat me to the it by a few years: Share your work here ✅ - #1696 by kristinwil
Hello Everyone,
Now a days, In India “Cheetah” has been making News. Almost 70 years ago, “Cheetah” has nearly wiped out in India. But, Now Indian Government has brought 8 “Cheetahs” from South Africa.
I was watching news in T.V. , It was shown that, one of the reason that “Cheetah” has become vulnerable animals in the world is that people hunt it, misunderstanding it with " Leopard" which is far more dangerous than “Cheetah” but they kind of look same. So, I thought of making a model to classify them.
I have completed my two lessons of the book and really having fun of making projects. So, just one to share one of the project with you all.
I write a Blog about this. I hope you all enjoy.
kaggle link-
Completed lesson 1 of the course and have been thinking of cost effective ways that could prevent major fire accidents at all locations. With CCTVs widely being used everywhere, I believe that the hazardous work locations (including kitchen at home) could be monitored using the image classifier for smoke and fire, and immediately detect and raise alerts to prevent major fire accidents. Here’s the notebook[1] that helps to detect if the given CCTV footage has fire or smoke in it.
Thanks for the notification, fixed it!
I have created an notebook that classifies catamaran images vs monohull images. This is my first published notebook. It was lots of fun creating it. My next goal is to deploy the model on hugging face using gradio.
The notebook can be found on kaggle (catamara vs monohull | Kaggle)
Constructive criticism is welcome 
Hi and thx for the great course!
I love the philosophy (build first and learn details along the way).
I’ve build two frontends speaking to the same Hugging Face/gradio API:
(Sorry can put only one link on the post. Link to the React WebApp is in the README of the Telegram Bot)
Hi, everyone, here is what I’ve tried from kaggle notebook and done some hand-on for lesson 1.
Snake_not_Snake? I’tried to predict between “snakes”
This text will be hidden
Almost any suggestions are most welcome! Correct me.
This is an article explaining how I modified the notebook that @jeremy made for us in week 1 of the first course. I changed it to check if images contained bulldogs or mini schnauzers, because why not?
I tried to explain how I did my work and clearly as I could. Any feed back is appreciated.
Great job, loved the clear instructions, the blog post design and the focus on reproducibility.
If i had to suggest anything, I’d say you can add some figures (of a mini-schnauzer for non-dog-aficionados 
) and showcase the results… maybe talk about accuracy, challenges in data collection for this example, next steps for this work as you learn week 3/4 etc. I know giving these suggestions is the easy part haha… good luck!
Thank you for the feedback. I’m thinking of building out a “v2”. I’ll keep this in mind.
I like your blog. How did you create it ? I also want to blog about my project.
Hello world, this is all very new to me.
I’m a chartered accountant by trade, with a maths & statistics background, looking to transition into Data Science, and Machine Learning in particular.
My first tentative step was the Google Data Analytics certificate, which gave me an introduction to R, SQL and Tableau. I quickly followed this up with some Python courses via DataCamp and was in the middle of the Machine Learning Specialization taught by Andrew Ng until just a couple of days ago, when I had the fortune to stumble upon this course!
I have been caught in the trap of the traditional model of learning (as alluded to by Jeremy) and find this hands on approach very refreshing!
Anyway, I started off simple by testing the Dog and Cat Classifier model from Chapter 1 of the book by uploading a picture of a dog/cat that went viral on the internet a couple of years back! The model as expected, performed very well on the training dataset, but surprisingly the model incorrectly predicted (with almost 100% conviction) that Dui was a cat and not a dog! It may be that I screwed up somewhere in my coding? but would be good to get your views
This is one of the more unique projects I’ve seen here - Reading about Pytorch, Lidar; and history, ancient civilizations in the same article…kudos.
