I am so grateful the FastAI team exists. It wasn't until I discovered their "Machine Learning for Coders" course that I really started to grok ML. I was in grad school trying to pivot my career from finance to data science. I didn't come from a computer science / math background and things just weren't clicking for me. I remember feeling angry, embarrassed, dumb, and overall that I wasn't smart enough to learn this stuff -- I was incredibly discouraged and felt that I didn't belong there. I was lucky enough to stumble across one of the course videos on YouTube (thanks recommendation algorithm!), and the rest is history.
The amazing thing about these courses is how simple Jeremy (and team) are able to make machine learning. I didn't need to understand python dependency management in order to learn how to train an really good image classifier. Their approach helped me have lots of little wins, gave me confidence, and helped build the motivation to slog through the harder stuff when I needed to.
From the bottom of my heart, thank you @jph00. You changed my life immeasurably for the better. I learned that I AM good enough, I AM smart enough, and I CAN do hard things... I just had to find the right way to learn them. Your courses completely changed my perspective on what was possible for me and opened the door to some of my life's greatest passions.
The first Fast.ai course back in around 2016 changed my life.
I was studying a masters in statistics and computer science that had 1 neural networks lecture and nobody knew anything about deep learning. Fast.ai and Jeremy’s teaching style helped me start playing with deep learning models really quickly and I changed my thesis topic to computer vision.
I ended up consulting on the topic and doing various startups leading to the startup I’m working on now which just finished YC (AiSupervision W22).
I doubt be here without fast.ai. I highly recommend and appreciate all the work that Jeremy and the rest of fast.ai do!
Watching from afar the great advances in machine learning over the past few years with AlphaZero, GPT3, DALLE2 I felt it important for me to start understanding what is going on under the hood. Having just completed the private pre-release of the course run through USQ, as my first foray into machine learning this was a great introduction that had me quickly produce a working image classification system. The videos are packed really with insightful rid-bits about practical approaches to iterating quickly to understand the data better to produce better results. Very much recommend the course.
This is awesome. One question I have always had - is the research on applying DL for images the most developed compared to other things?
Even DL used for audio processing (classification, separation etc) seems to convert audio to spectral graphs and apply DL to that.
Changing a problem to be expressed as image inputs will be an advantage when using DL as a solution. Would you agree ?
I haven’t seen this course content yet, but fully did the 2019 version.
Extremely grateful to have found it. Changed the course of my life.
I can vouch for it's quality.
Jeremy is an excellent instructor. So much clarity in his teaching!
I love that this is a hands-on course, and there are ZERO hand-wavings. I also really like the top-down approach of teaching. Now, whenever I try to communicate something or teach someone, I try to do it top-down. And I have Jeremy to thank for that.
Currently, I am attending his APL study group and having a blast!
Only question for @jph00 is: second part, when?
Fantastic course - Fastai courses are a must for anyone looking to learn Deep Learning/ML.
Hello Jeremy. Thanks for this great content. I have gone through your entire course and learned a ton from you.
Now moving on from here, do you have any resource recommendation where I can dive deeper into machine learning and deep learning theory? And also any resources to become a much much better programmer?
I am currently working in as an assistant in a research lab. My coding skills are not that great.
Can one do these lessons in any order? For example, do CNN first then jump back to NLP. Or skip the implementation from scratch because I have done a similar one in another course.
what is/will be the state of deep learning in 2022 or next 3-5 years? you hear/read so many news/articles in HN about decline of DL. Is that so?
I re-did the course with this 2022 version. Highly recommend it :)
The course uses PyTorch, which despite being a big played still has many issues running g on any of the latest AMD cards.
Windows not supported on AMD cards Navi series cards not supported in general. Heavily biased towards CUDA, despite AMD cards drivers being open sourced far more than Nvidia cards.
Remote machines and kaggle notebooks go some way to improving these limits for the course. I'm complaining a bit more in general here, I think.
You guys really are the best. Thanks for all the hard work.
There are too many poor design decisions in the fast.ai library.
One should invest too much time just for the sake of learning the library's weird API, and then using it.
Doing something custom is too difficult, in contrast to Jax, PyTorch, and even (poor library) TensorFlow.
The coding practices are whimsical. The codebase wouldn’t pass code review in any respectable company.
Variable namings are weird and super-problematic.
I fully stick to what I said. Learn techniques, best practices, and, most importantly, Howard's attitude. Then take them with you and move onto something like PyTorch.
Howard is great with one problem: he kinda hates math. It might also seem that he ends up promoting anti-intellectualism.
Hi folks - I'm the creator/teacher of this course. I'd be happy to answer any questions that you have about the course, learning deep learning in general, or the state of deep learning in 2022.