r/datasets. Also, I would be very surprised if such dataset has even been even gathered and made available publicly. This sounds like typical long-term project with EU humanities scientific grants (I have personally worked with a similar one).

You will probably get better answers at r/LearnMachineLearning. You need some basic neural networks courses for beginning. Then e.g. HuggingFace’s audio processing course, it’s short and high-level, but will be a nice intro. In general you will focus on convolutional networks (CNNs) and processing audio either as 1D signals or 2D images (spectrograms).

You shouldn’t do that, for multiple reasons (I can elaborate if needed). Your model is a binary file, a set of weights, basically no matter what you train. Once you write it to disk, typically with built-in serialization (e.g. pickle for Scikit-learn, or .pth format for PyTorch), there are lots of frameworks to deploy it.

The easiest to use and the most generic one is BentoML, which will package the code into a Docker image and automatically deploy with REST and gRPC endpoints. It has a lot of integrations, and is probably the most popular option. There are also more specialized solutions, e.g. TorchServe.

However, if you care about inference speed, you should also compile or optimize your model for the target architecture before packaging it for the API and target runtime, e.g. with ONNX, Apache TVM, Treelite or NVidia TensorRT.

Very interesting development, but I’m waiting for more production-ready version. Having to set up separate Github repo, with manual installation inside, is not exactly nice. However, if this gets fully compatible with HuggingFace Hub, then this will be huge for simpler cases.

I have worked (academia & industry) with both GPs and NNs, and never even heard of neural proceses, so… not really.

For spatio-temporal data GNNs are the hot topic and more known approach

Reported. Breaks rule 7, this is not nearly a “quality contribution”.

(I assume you are talking about convolutional models in the context of computer vision)

I had similar constraints (embedded devices in specific environment) and we didn’t use deep learning at all. Instead, we used classical image descriptors from OpenCV like color histograms, HOG, SIFT etc. with SVM as classifier. It can work surprisingly well for many problems, and is blazing fast.

Consider how you can make the problem easier. Maybe you can do binary classification instead of multiclass, or use only grayscale images. Anything that will make the task itself easier will be a good improvement.

If your problem absolutely requires neural networks, I would use all tools available:

1. Skip connections, either residuals or to all layers (like DenseNet)
2. Sharpness-Aware Minimizer (SAM) or some of its variants
3. Label smoothing
4. Data augmentation with a few really problem-relevant transformations
5. Extensive hyperparameter tuning with Gaussian Process or multivariate Tree Parzen Estimator (see e.g. Optuna)
6. You can concatenate those classical features like color histograms or HOG to the flattened output of the CNN, before the MLP head. This way you reduce what CNN needs to learn, so you can get away with less parameters
7. Go for more convolutional layers instead of large MLP head. Convolutional layers eat up a lot less of parameter budget than MLPs.

You can also consider training a larger network and then applying compression techniques, such as knowledge distillation, quantization or pruning.

Personalized recommender system? No, not doable. This requires separate setup, model serving, regular retraining since typical collaborative filtering does not support adding users and items. Besides, it makes no sense if you don’t have prior data.

However, implementing popularity-based system, without personalization, is possible. This is typically just simple Bayesian statistics, doable in 10-15 lines of Python. See e.g.:

- https://arpitbhayani.me/blogs/bayesian-average/

- https://www.evanmiller.org/how-not-to-sort-by-average-rating.html

- https://www.evanmiller.org/ranking-items-with-star-ratings.html

- https://www.evanmiller.org/bayesian-average-ratings.html

r/learnmachinelearning

If you have something “enterprise-level”, then pay some actually good ML consulting company, or high end ML-focused software house, to do this for you. If you don’t have money for that, you most probably don’t have enterprise data and you just need to learn, not ask general question on Reddit.

I encourage you to urgently read subreddit rules. Reported.

That’s exactly what ConvNeXt V2 does

Yes and no. In my opinion, ConvNeXt is less about data and more about careful architecture design and smart training, and less about data. But yeah, CNNs are better than ViTs if done well, that’s true.