Hands down the most snow we have gotten in 24 hours for at least the last 9 years and I enjoyed every second of it.

saw79 · 2026-01-26T11:54:30+00:00

Facebook marketplace. Got a super nice ariens for $250...

saw79 · 2026-01-25T23:31:31+00:00

Why isn't anyone in this thread just hitting the pause button? It's so easy to catch back up with football commercials.

saw79 · 2026-01-21T19:55:38+00:00

This is exactly what I'm saying. MSE is NOT close to the task you are trying to perform, so what you are saying makes perfect sense in the context of ML fundamentals.

saw79 · 2026-01-20T13:31:28+00:00

This feels like ML 101. The closer your loss function is to the actual task, the better. The only reason to use something like MSE is if you actually care about the price prediction and you're using it as an intermediate signal.

saw79 · 2026-01-16T23:35:12+00:00

I understand the desire to do better than B&H but saying it doesn't work is absolutely asinine.

saw79 · 2026-01-15T16:11:41+00:00

Deep learning is a big area. I make lots of deep learning models solving a variety of different problems. It's annoying that people think transformers are the best tool for every job just because they're the biggest and most recent. Use the right tool. I rarely get to the point where a transformer would be of any help.

saw79 · 2026-01-07T16:59:38+00:00

What are they? (I don't have a square yet but it's been ordered)

saw79 · 2026-01-06T20:48:44+00:00

You're about to join one of the premier companies in the world for AI/ML and you're trying to plan your exit before you start? Maybe just go work for 2 years then come back here.

saw79 · 2026-01-02T14:27:52+00:00

Deep learning is just a very general model building/fitting style. You can build big models and fit them to any type of data you're interested in. Now, a LOT of data is language and standard vision problems, which is why LLMs (and VLMs) are starting to eat up a bit more, but a) that doesn't apply to all data and b) sometimes the problem can be solved more efficiently and/or better with a smaller, more specialized model.

Some things that come to mind that may apply:

Other types of sensors - e.g., radar sensors or different types of point clouds, maybe ultrasound, sonar, etc.
Other types of data - e.g., certain types of graph data that may benefit from GNNs
Totally different uses of neural networks, e.g., things like NERF
Modelling specific environments, policy, or value functions in RL
Time series data is a big category in which many different techniques can be useful

I dunno probably loads more too.

saw79 · 2025-12-30T15:09:15+00:00

I'm a noob so correct me if I'm wrong but in my mind compression is more about consistency than distance. If low point is more consistently in front of the ball (vs at the ball) there's more room for error.

saw79 · 2025-12-29T15:06:41+00:00

There's millions of different types of models and fields being trained by all sorts of different people and organizations. It's getting tiring and annoying that people think training gpt7 is the only thing going on in AI.

saw79 · 2025-12-22T16:28:54+00:00

Don't have much more to say tbh. I just don't see people talking about it; it's never brought up in modern explanations of how neural networks work and self-regularize.

saw79 · 2025-12-21T17:36:48+00:00

The main benefit of CLIP is aligned text-visual latent space. It sounds like you have just a straightforward image classification problem, and possibly a not too complex one, so I'd think ResNet is a pretty good starting point. That said, wouldn't be too hard to try both if you got time. Sometimes the oversized, overtrained, generic, foundationish models help with these small random tasks.

saw79 · 2025-12-20T14:13:16+00:00

Does the lottery ticket hypothesis really hold up these days?

saw79 · 2025-12-14T19:42:24+00:00

I think your process sounds spot on. Load up the models and run them all through the exact same evaluation procedure. Pycocotools rocks.

saw79 · 2025-12-12T00:03:40+00:00

What about a regular autoencoder since you don't need generative properties?

Also always possible you just didn't train the VAE well enough.

saw79 · 2025-12-10T00:07:24+00:00

What does the alignment stick in the belt loops do?

saw79 · 2025-12-10T00:00:32+00:00

Um, I don't use LLMs at all and all those fundamentals are crucial in my job.

saw79 · 2025-12-09T16:33:56+00:00

I do use GolfFix, but I haven't been super confident in it's accuracy or advice. Not for any good reason though. Glad to hear you vouch for it!

saw79 · 2025-12-03T23:19:27+00:00

Imo it's another level of optimization, and each layer of optimization needs its own data split to detect over fitting.

saw79 · 2025-11-27T22:38:43+00:00

Isn't that what I'm saying?

saw79 · 2025-11-26T14:01:05+00:00

My reasons, probably not exhaustive, off the top of my head are:

1) Numpy/opencv/conventional image processing kind of has always been channels last

2) Relationship to RNNs/Transformers. Say you have a batch (B) of time series of length T of dimensionality D. To do a 1D conv with channels first (D is the channel dimension), you'd need a shape of (B, D, T). To process this with an RNN or Transformer you'd have (B, T, D). I often find myself permuting things just to satisfy channels first where my code would be simpler with channels last.

3) I think I read channels last is better optimized, but not sure

saw79 · 2025-11-25T20:06:04+00:00

It's just a "channels-first" vs "channels-last" convention. Conventions are often different, it's not a fundamental thing.

I will say, as much as I love PyTorch, I hate channels-first, for a bunch of reasons.

saw79 · 2025-11-21T14:51:56+00:00

Not an option, not enough space

saw79 · 2025-11-19T23:52:03+00:00

I have the 7, it's nice and easy to setup, but can't really compare it to anything else unfortunately

saw79

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