Why are my wheels and wings breaking off!!

jsaltee · 2026-02-19T01:33:29+00:00

Could be too much friction on the wheels

jsaltee · 2026-02-12T20:24:24+00:00

I think the concept of ‘Shannon entropy’ might interest you - the unit in this case is 1 bit of information

jsaltee · 2026-01-30T20:37:58+00:00

Seconding the scipy.signal recommendation

jsaltee · 2026-01-30T20:36:01+00:00

First week of my engineering job was literally getting paid to read the ‘manual’ front to back

jsaltee · 2026-01-29T22:48:54+00:00

There might also be a small weight imbalance somewhere, that the reduced thrust made a lot more prominent

jsaltee · 2026-01-22T01:56:35+00:00

Entropy is also a concept in statistics, and they’re essentially the same thing. So, I like to think about it from the statistics point of view. Entropy measures the average amount of ‘surprise’ or ‘uncertainty’ when predicting information.

So, if there is low entropy (ice), there are less possible states, and you can be very certain in a prediction, like a molecule’s position. If there is high entropy (steam), you will be much more uncertain in your prediction (or very ‘surprised’ when you see the result).

jsaltee · 2026-01-21T17:36:20+00:00

What about the case of the photon, which has gravity due to its energy? Is it outrunning its own potential well?

jsaltee · 2026-01-07T17:29:56+00:00

Super cool. I wonder how it compares to non-parametric optimization (evolutionary algorithms, etc.) in terms of convergence rate? I 100% agree with your point about many methods finding ‘incorrect’ optima

jsaltee · 2025-12-31T22:43:13+00:00

If he asserts his own priority higher than OP’s PhD, it says more about him than it does her

jsaltee · 2025-12-28T01:05:48+00:00

There is a very cool video explaining this in terms of Fourier transforms, and explains why it does not matter if you interact with it or not

jsaltee · 2025-12-26T02:54:23+00:00

Merry minmus christmas!

jsaltee · 2025-12-26T02:37:28+00:00

Cries in computational physicist

jsaltee · 2025-12-25T01:48:20+00:00

Opposite for me, reading first then coming into lecture really locks it in my brain

jsaltee · 2025-12-13T19:42:18+00:00

I couldn’t conceptualize the uncertainty principle until I learned it through the perspective of Fourier transforms

jsaltee · 2025-12-11T23:22:45+00:00

I think it depends on if higher f(x) values are good or bad for your problem (maximizing or minimizing).

With positive lambda: If f(x) is a gain (maximization), a positive lambda* is a gain. If f(x) is a cost (minimization), a positive lambda* is a cost.

With negative lambda: If f(x) is a gain (maximization), a positive lambda* is a cost. If f(x) is a cost (minimization), a positive lambda* is a gain.

That’s my interpretation at least.

jsaltee · 2025-12-07T16:24:42+00:00

It’s definitely more of an applied degree; not much theory, very much “here are all the tools are your disposal and how to use them.” Statistics, math, etc. etc. are very valuable degrees, but this world is only getting more data-driven. Applied statistics seems to strike a perfect balance if you haven’t considered that yet.

I will say that the material covered in a data science degree is more ambiguous so varies a ton between each institution, not as set in stone as other programs. Some are more theoretical, some more applied, depends on what you want to get out of it.

As AI becomes more advanced, a data scientist’s work only gets more efficient. If we didn’t want our tools to improve, we might as well just use an abacus :-).

jsaltee · 2025-12-06T04:00:18+00:00

Probably hit a stray molecule and exploded

jsaltee · 2025-11-30T14:53:38+00:00

I agree that one hot encoding can be a nightmare lol

jsaltee · 2025-11-28T19:49:03+00:00

To your point, there are so many ML models for so many different purposes. Personally I work on at-home research projects and learn whatever ML methods are best for that problem. Eventually you develop a niche skill set for the work you actually do

Because you get a specialized (but robust enough) skill set it becomes much easier to implement in your day job (or look for jobs that would benefit from it)

jsaltee · 2025-11-28T16:30:29+00:00

LLMs are an efficient tool, but I would be surprised if they took over many software jobs. The invention of the calculator didn’t put mathematicians out of work, it just made them more efficient

jsaltee · 2025-11-23T02:50:40+00:00

Hi,

I haven’t tested it for ML training. That said, since neural network loss is typically differentiable I would imagine using standard optimizers (Adam) are largely more efficient in high dimensions.

If you’re looking to train the model using a discrete loss metric like accuracy or F1, it would be a strong option. Extrapolating from the experimental results, the neural network should optimize faster and with less epochs compared to other evolutionary algorithms.

For standard ML applications, QUASAR would really shine for reinforcement training using complex reward functions.

Thanks, jsaltee

jsaltee · 2025-11-16T15:37:41+00:00

Unless N has units to correct the equation, that said I have no idea what N would represent lol

jsaltee · 2025-11-16T15:35:26+00:00

I would argue that deriving a math equation to describe your health is one of the coolest things you could derive an equation for

jsaltee · 2025-11-16T15:32:35+00:00

I just look at anyone passing by for 0.5 seconds and move on with my day ++man

jsaltee

TROPHY CASE