Look into tutorials or projects published on YouTube maybe, even that is a great source. The main idea to copy someone's project in beginner stage, is to get inspired by them and then later do what you want and build upon it. Right now you have the theoretical knowledge, but programming will also come into the picture when you are on a data science job

Projects. Only way to test what you have learned is projects. In Andrew Ng's deep learning course, he has assignments pertaining to a topic/application of deep learning. Easiest way to expand this is to make a whole project out of it. Best way to show your skills.

As you said, yes there are resources to get weights for models, look into model zoos. Hope this helps!

Not directly related to label smoothing but some ideas may be worth looking into: " Preventing Manifold Intrusion with Locality: Local Mixup"

I would really suggest to use Extended Kalman Filters or Particle Filters for this. You can model the environment factors, the acceleration/velocity factors and also take into account orientation history

Why tistory, I feel I'm missing something

How about using the embeddings of the whole post? Then you just have to train a model to predict trait from one post. A person’s overall trait can be the average of all traits predicted by all of his posts. I don’t see a point in using RNN over posts.

What I usually do is read the most noteworthy research papers and then check the implementation on GitHub. I’m taking Aaron Courvilles classes and they are good but without going through a degree the best choice would probably be Karpathy (plus all the links shared by u/verboseEqualsTrue

yeah, anything with memory can help ur case, all this algorithms are expensive in computations

Karpathy: https://m.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ

Stanford - CS224N: https://m.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ

slides: https://web.stanford.edu/class/cs224n/slides/

Carnegie Mellon - 11785: https://m.youtube.com/playlist?list=PLp-0K3kfddPwgBSCbDtT6NaVOd-gIHVMW

You should apply those transforms on each batch while training, rather than transforming the data before training. This approach is called doing the augmentation on the fly.

If you have a small dataset, then Transformers are out of the question, especially if we're talking pretraining and all.

Seems to me like you might be interested in ML methods, such as XGBoost. Since you have tabular data it will probably outperform all other methods at first. From there on out you would be trying to find a better tailored model from the task, depending on how you want to use your data. Given your data situation, you would be looking at deep LSTMs for the end game. But currently, it doesn't matter if it's 20 or 2000 samples (idk how you count them), that's not enough to solve something you claim is too difficult to outright mathematically model.

Reinforcement learning might not be adequate given that you say that the problem is too difficult to model mathematically. RL will only be useful to you if it is difficult to model it because the problem is wide, ie it is hard for you to narrow it down to a general formula. If the problem is hard in the sense that it would be difficult or narrow, then your agent might not be able to figure out how to solve the task at all, and you would have to think out the training regimen really well to teach it anything. RL is not really well suited for very hard problems.

Finally, it doesn't seem to me you have an environment set up for the agent, because if you did, your problem would be solved given that it would require you to mathematically model it. And if it was easy to obtain data in the first place, you would be having way more than 20 or 2000 samples. That's why I presume that RL is completely out of the question for you as well.

I would personally not tackle this problem with trajectories. If you want to solve this using DL, then you should create a bigger dataset using actual camera recording, and then either label the bounding boxes or segment the image. Then you can use any of the pretrained backbones and simply train an object detector. Given an offset in the next frame, you can calculate the movement for the camera.

This is a task so generic that just with a few hundred to thousand samples you can probably get a semi-supervised labelling scheme going on - with some other model labelling the images automatically and then you just need a few humans judging these labels or correcting them. And this task is so trivial and widespread you can find a workforce to do this anywhere.

The question is what performance you would expect. But in all cases I would say that if you need a very robust solution, you should probably look into mathematically modelling it - we are presumably talking about a differential system in the background, which is not going to be easily solved by any mainstream DL model. All methods mentioned here can essentially be dumbed down to a very large non-linear equation. They can only mimic a differential system up to a certain amount of precision, determined by their width and depth, as well as the statistic significance of your samples.

Assemble a dataset and raise a challenge on Kaggle?

Well, the original ChatGPT is overloaded and Bing is not really helpful. It loosely suggest to use LSTM or Transformers.

Using reinforcement could also be a nice idea.

Thank you for suggesting this paper! I have already read it and found it quite informative.

Regarding your question about what I meant by 'specific data', my plan is to use label smoothing only on selected training data, rather than applying (generalized) label smoothing to all training data.

Currently, I'm seeking more knowledge on such 'data-specific' label smoothing. Hope this clears things up!

Not sure what you mean by specific data. Here is a good read - When Does Label Smoothing Help? (https://arxiv.org/pdf/1906.02629.pdf)

Thank you! I will try

I didn't mention it in the post, but I'm using the albumentations module. I rotate, shift, rotate, blur, horizontal flip, downscale and use gauss noise. I get around 400 images after doing this. Is there anything you would suggest?

I have an accuracy of 98.50 and I have dice of around 0.30-0.65 in each image

I am currently using the albumentations module. I rotate, shift, rotate, blur, horizontal flip, downscale and use gauss noise. I get around 400 images after doing this. Is there anything you would suggest?

Also, sounds more like a Reinforcement Learning problem rather than LSTM.

Why don't you dump this on ChatGPT and get a way more accurate answer ?

Your dataset is rather small and it seems you are not really doing Augmentations? I would try different Augmentations, that should improve your results regardless of the used model. Have you looked at some frameworks for medical image segmentation? nnUNet comes to mind which would give you a solid baseline. How good are your results currently?

I would suggest you to apply more dramatic image augmentation methods

SMP seems to only support 2D image segmentation, but medical images are usually 3D.

Curie is 6.7B and it is surprisingly strong.

Have you tried using the segmentation models from the SMP package (Iakubovskii, P. (2019)? I built a segmentation model for dermoscopy images and pre-trained models consistently outperformed anything else, architecture didn't matter that much. I got best results with "U-Net with SegFormer pre-trained encoder".

It depends how much training data you have, but unless you have millions of samples, pre-training usually trumps architecture.