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.
Practical Deep Learning for Coders 2022
11–20 of 86 posts
Re: Practical Deep Learning for Coders 2022
#12Can 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.
They're designed to be done in order, but yup if you know how SGD works, for instance, you could certainly skip over that bit. The videos all have youtube timestamps, so if you drag the scrollbar you'll see what each section is about.
Or you could do those bits at 2x speed in case there's some concepts there you haven't seen before.
The NLP lesson could possibly work reasonably well standalone if you already know some DL basics, since it uses a different framework (Hugging Face) to the earlier lessons.
The CNN lesson would probably largely make sense if you already understand multi-layer perceptrons, since it mainly shows how a convolution is just a special case of sparse matrix multiplication.
Re: Practical Deep Learning for Coders 2022
#13Hi 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.
First of all, a big thanks! What is your take on the current state of autonomous driving? Do you think we can achieve "full autonomy" with the technology we have currently? Any new advances in DL that you are excited about?
Honestly I'm not an expert on autonomous driving so I'm not sure I have great insights there. I do know quite a bit about computer vision however so feel qualified to comment on that bit -- I suspect the decision by Tesla to only use CV, and not LIDAR, may turn out to be a mistake. I don't see any reason why we couldn't achieve full autonomy with our current tech including LIDAR, although I don't know if it can be achieved at a practical latency and power budget.
The new advances in DL I'm excited about are things I show in the class: the accessibility of modern NLP thanks to the Hugging Face ecosystem; the power of ConvNeXt for even better computer vision models; the way Gradio and HF Spaces makes it trivially easy to get a working prototype application using DL online.
I'm also excited about hosted models and applications like GPT-3, DALL-E, and Codex. All the illustrations on our course website are from DALL-E, for instance!
Re: Practical Deep Learning for Coders 2022
#14Hi 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 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 Howard, s great instructor, communicator; learn his attitude, and then move to PyTorch (keeping the attitude, the knowledge, and the lessons with you.)
Re: Practical Deep Learning for Coders 2022
#15Hi 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.
Thanks for creating this fantastic content, I'm excited to give the 2022 course a look. It's an exciting time for AI. I'm curious about your thoughts on gpt3 and also the state of the art in computer vision, and object detection. All the best
Re: Practical Deep Learning for Coders 2022
#16Earlier 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 :-)
Both the Aurélien Géron and François Chollet TF books are absolutely terrific, and everything you learn from them will be extremely useful in becoming a deep learning practitioner, regardless of what framework you end up using. So if you've started with one of those books already, keep it up! :) The fast.ai course would actually be a pretty good addition to either book, since you'll get to see a whole different way o…
Thank you
Re: Practical Deep Learning for Coders 2022
#17This 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 ?
Good question!
I think a major reason for this is because of transfer learning. For computer vision, there are many good pretrained models that were trained on huge datasets (like ImageNet) that can be fine-tuned for custom tasks. Other fields often do not have such pretrained models and huge datasets to work on, so it turns out transforming a dataset into an image dataset and fine-tuning a pretrained model works better than training from scratch.
Re: Practical Deep Learning for Coders 2022
#18This 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 ?
Oops dumb question. Watched the first video and got my answer.
Re: Practical Deep Learning for Coders 2022
#19Earlier quoted context omitted.
Thank you, so much, for enabling us mortals the power of ethical, modern AI. Your work, and the work of your colleagues, has brought so much good to this world. It wasn't until the last few years I saw people start freezing the model and just fine tuning the last layers. I've watched presenters from flamingo and imagen talk about their similar approaches. I heard it here first, at fastai.
Haha yes - it's been wonderful to see how (eventually) the deep learning world has taken to transfer learning!
What’s your take on meta learning?
Re: Practical Deep Learning for Coders 2022
#20You guys really are the best. Thanks for all the hard work.