21–30 of 86 posts
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.
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.
I have a search problem of my own and I have had a hard time applying what I have learnt (including the coursera DL specialization). The chief characteristics are: (a) It is a fuzzy search of a corpus that is in a non-English language. (b) The search should be able to run on a mobile phone _offline_.
Is this possible? Can training be done elsewhere and transferred to TinyML or some such? What would be a good forum to go seeking answers?
Earlier quoted context omitted.
Thank you for creating this course. I started out on Tensor Flow but seeing this material I am in two minds whether I should abandon my TF book and start this one or save it for later. Most likely I am going to dive in :-)
My suggestion would be to learn all the stuff from this course, using fast.ai library, and then gradually move towards PyTorch. fast.ai is a fantastic educational resource and a great way to approach solving problems. But the library itself is lacking, and if you are an experienced programmer, when building real-life projects, you will be frustrated with fast.ai library. The goal, IMO, should be learn from Jeremy How…
It's also the only library I know of that consistently bakes in best practices like super convergence techniques or making things like test time augmentation very seamless. Many libraries lag behind fastai 1-2 years in this regards, and frankly it can be frustrating to use other frameworks sometimes.
There is a slight learning curve, for example to learn the DataBlocks API or the callback system, but once you really understand what is happening you will understand how nice the API is and how well engineered it is.
Side note: Regarding being an experienced software engineer, I highly recommend digging into how the python language was extended for this project (fastcore) and the development workflow used (nbdev), which I think could be interesting for those software engineers you mention as well as heighten your understanding of the ecosystem of tools.
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.
I remember you were bullish about Swift a few years ago. What's your current view on non-python deep learning?
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.
Thank you so much for this course. I plan to go through it properly. I have a search problem of my own and I have had a hard time applying what I have learnt (including the coursera DL specialization). The chief characteristics are: (a) It is a fuzzy search of a corpus that is in a non-English language. (b) The search should be able to run on a mobile phone _offline_. Is this possible? Can training be done elsewhere…
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.
How far away is the fast.ai from working on a Mac? PyTorch recently gained support (https://pytorch.org/blog/introducing-accelerated-pytorch-tra...) but that's only the start. Is this something that is being worked on?
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!
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.
I tried the 2021 course but I didn't finish. I think the biggest friction for me was using the remote machine. I wasn't able to make steady progress like I do with my offline learning projects. How far away is the fast.ai from working on a Mac? PyTorch recently gained support ( https://pytorch.org/blog/introducing-accelerated-pytorch-tra... ) but that's only the start. Is this something that is being worked on?
Mac support for all the libs used in the course will probably continue to improve in the coming months and there should be no reason you won't be able to run the stuff for the course locally on a Mac at that time. Having said that, even the M2 trains deep learning models much slower than even the free NVIDIA GPUs provided by Kaggle. So you'd only want to use local development for the smallest and simplest models. (The course shows how to train models that are fairly cutting edge and some take a while to train even on modern GPUs, so they wouldn't be a good fit for a Mac.)
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.
Thank you so much for this course. I plan to go through it properly. I have a search problem of my own and I have had a hard time applying what I have learnt (including the coursera DL specialization). The chief characteristics are: (a) It is a fuzzy search of a corpus that is in a non-English language. (b) The search should be able to run on a mobile phone _offline_. Is this possible? Can training be done elsewhere…
a) BM25 after some preprocessing (lemmatization etc.)
b) fastText / GloVe (possibly weighted by BM25)
The results can be surprisingly good. Often no need to bother with big language models or GPUs.
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.