1. Can’t you also create custom loss functions for XGBoost? I’ve never used it myself but it seems as easy as doing it for an ANN

2. Is it always trivial to get meaningful embeddings? Does taking the last hidden layer of ANN guarantee that representation will be useful in many different contexts? I think it might need more work than you expect. I’m actually looking for a write up about what conditions needs to be met for a hidden layer to provide meaningful embeddings. I think using a triplet loss intuitively favors that but I’m not sure in general.

3. XGBoost allows for this too, doesn’t it? The scikit-learn API definitely at least let’s you create MultiOutput models very easily. Granted it can be silly to have multiple models under the hood but whatever works.

Sorry I’m playing devil’s advocate here, but the vibe I’m getting from your post is that you’re excited to finally getting to play with DNN. Which I can relate to. But don’t get lost in that intellectual excitement: at the end of the day, people want you to solve a business problem. The fastest you can get to a good solution the better.

In the end it’s all about trade offs. People who employ you just want the best value for their money.

Ha good point

You’re suggesting Softmax then normalize to whatever is required. Softmax takes the exponential of the value then performs the normalization. You might not want that exponential. You can create a layer that does the normality through a custom activation function you use in the last layer

If you just want to normalize everything why creating a custom activation function that just does that?

It sounds more like constrained optimization than ML but still too vague of an explanation for me to be helpful so I’m giving up. Good luck

Maybe give an example? Maybe I’m just slow but I think it’s ambiguous the way it’s phrased

https://www.kaggle.com/competitions/nfl-big-data-bowl-2023/overview/evaluation

Yeah just use logistic regression

Decision tree of life

Fair

Isn’t the simplest version of what I described essentially matrix factorization?

Hmm neural networks can be arbitrarily small. You can model logistic regression with a neural net. You could definitely try a simple neural network with one embedding layer for users and one for products and use a sigmoid activation for the output layer. Then you need to combine these layers in some ways, for instance a simple dot product or concatenation. You can start with no hidden layer and add more if the results are not satisfactory.

That’s a good question for Galactica

Chill out. Obviously ML is useful. The biggest companies in the world are only this big because of ML. I’m just saying that in my experience, many companies think they need it when they really just need data engineers and data analysts

Actually having a use case

We’re also constantly iterating on the learning algorithm. We learn to learn. That’s one of the most important skill we learn throughout our education. Computer need to be taught how and what to learn for the most part

I’m curious why this is a useful problem to solve. What is an application for this? I also, after looking for a minute, I cannot find more than a few instances of a fiber that goes under another fiber AND comes back up again. I think I would try non DL/classic segmentation techniques

The one good news about the direction this planet is going?

I really hope it’s the latter

🤣😂

But you wouldn’t you need to have a set of real papers you’re actually very confident they are real?

Won’t you need a labeled training set to make that work?

I would create a conversational AI that can be used as 24/7 therapist. It would talk you out of feeling like crap on the short term and help break whatever unhealthy behavioral pattern you’re trapped into. Help you make new healthy habits etc.. not sure what exact cognitive functions are required, more than one though and I feel like it can be seen as a reinforcement learning problem? You’d need a way to assess one’s mental state, and then define actions (just say things to the subject) that would make you feel better but also challenge you enough to improve your state on the long term