21–30 of 43 posts
TensorFlow is too low level. Fastai abstracts too much away and feels way too magical - once things start going wrong, or if you need to do something totally outside the bounds of the library, good luck.
Guess I’m mostly just a bit tired of frameworks for what feels like frameworks sake.
I think this is cool... but... personally, I feel that PyTorch’s API is more or less where I want a deep learning API to be. TensorFlow is too low level. Fastai abstracts too much away and feels way too magical - once things start going wrong, or if you need to do something totally outside the bounds of the library, good luck. Guess I’m mostly just a bit tired of frameworks for what feels like frameworks sake.
That being said, a current project I'm working on didn't really fit the fastai pipeline and I needed to drop into pytorch. It's a total shame since there are so many little niceties that make things easier.
I think this is cool... but... personally, I feel that PyTorch’s API is more or less where I want a deep learning API to be. TensorFlow is too low level. Fastai abstracts too much away and feels way too magical - once things start going wrong, or if you need to do something totally outside the bounds of the library, good luck. Guess I’m mostly just a bit tired of frameworks for what feels like frameworks sake.
For most cases, objects in the fastai framework inherit pytorch objects and are often rather thin. This means that you can usually roll your objects if the fastai ones aren't sufficient. This really help alleviate the feeling that you're "on rails" of the library. That being said, a current project I'm working on didn't really fit the fastai pipeline and I needed to drop into pytorch. It's a total shame since there a…
My coworker recently completed a bootcamp using pytorch, and I am working through the fastai course, so it has been interesting to compare experiences.
The lectures have a ton of gems in them, but I have a hard time following the lectures as a beginner/intermediate practitioner, as around 1-5 percent is relevant for me. I don't particularly like the fastai library as it feels like an obstacle between me and the underlying pytorch library. After trying to use fastai for some time, I started looking directly at pytorch and found that all the abstractions and features…
The second part of the course ( https://course.fast.ai/part2 ) - builds stuff bottom-up, starting from matrix multiplication all the way up to ResNets. This is a great resource even if you want only use Pytorch.
Is "fastai to pytorch" what "keras is to tensorflow?" Glad to see pandas support for tabular data.
I think this is cool... but... personally, I feel that PyTorch’s API is more or less where I want a deep learning API to be. TensorFlow is too low level. Fastai abstracts too much away and feels way too magical - once things start going wrong, or if you need to do something totally outside the bounds of the library, good luck. Guess I’m mostly just a bit tired of frameworks for what feels like frameworks sake.
There are quite a few comments here pointing out (quite correctly!) that v1 was not at all easy to hack on. We've spent the last couple of years fixing that. Have a look at the paper to see what mean - especially the "mid-layer API".
Nice to see our paper on HN! FYI the paper covers v2, which is a from-scratch rewrite that introduces a new layered API. There are quite a few comments here pointing out (quite correctly!) that v1 was not at all easy to hack on. We've spent the last couple of years fixing that. Have a look at the paper to see what mean - especially the "mid-layer API".
That being said, I admire the passion and effort that goes into FastAI and I think it does a great job at providing an entry point into the world of ML/deep learning that is far more accessible to people. I'll probably still read through the library because I always pick up interesting ideas and learn new things when reading other peoples code, especially when a lot of thought has been put into structuring a library.
Bleeding edge is great for fun and personal development, Jeremy (with the fast.ai team as a whole) is wonderful and all that, but you need to have your own suitable case study to make the most of such techniques and implementation... nlp for the time being, it was image processing when they were just starting and I followed their courses. In 2020 or year V, VI or VII of this new machine learning explosion, generalist…