No...as I wrote take a previous year's complete data.. Let's take 2021 season..and you have gone back to 2021...you have absolutely no knowledge of the outcomes of games..

The season starts and you are all fired up to earn some money... You wait until a reasonable amount of games are played... around the 60% I reckon is a good percentage So you start training the model. You start with a base amount of cash...e.g $100 You predict for the coming 5 - 10 games...how did the model performed. , Have you made a profit or not.. again..the next 5-10 games..You play until either you run out of money or the season ends. If you run out of money..the bitter truth..back to the drawing board If the season ends.. measure your money.its around $100 - $120.. well at least you didn't lose..but it was tight $121 - $150 maybe you have something $151-$200 maybe you should give it a go > $201 lets make some money 🤑

So I've separated my dataset into train and validation datasets (90%, 10% split). Is this what you mean?

Or should I have a separate test dataset on top of that you think?

Don’t know that you’ll get much processing done (in a reasonable enough timeframe) on Android, but you can definitely create a client/server architecture where the processing is done remotely and the android phone just feeds the remote server data for processing and receives the output and displays it.

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l(Θ) = ln[L(Θ)]

What else do you need? 😅

This is semantic segmentation but could be good starting point https://github.com/pytorch/android-demo-app/tree/master/ImageSegmentation

only about 15

Totally UNDERRATED.

The whole research industry is chasing the x% performance gain while the train/compute/energy cost increase by 10x%

Aiming for efficiency and interpretability would be a good way.

But meh.

Hah sticking things together will only get you so far. True innovation and improvement of existing solution comes from fundamental understanding of these theoretical concepts.

I imagine it's important when you're theorycrafting about whether a novel architecture will be able to propagate gradients in a way that might facilitate learning things, but yeah for the most part it seems about intuition and copying successful approaches more than anything.

One day somebody will implement git glue that stitches code with LLMs and you'll never type any other command again.

in that case you would be right, but those requirements are bogus and far off from reality

Is it though? These days it seems like even a lot of research papers are just "we stuck together a bunch of pytorch components like lego blocks" or "we fed a transformer model a bunch of data".

Math is important if you want to invent new kinds of neural networks, but for end users it doesn't seem very important.

Now you are receiving some requirements like mse < 100 dollars for your estimation and you also have to predict houses on country which is absolutely not represented in dataset. On top of that, you “might” need to run this model on mobile phone to make inference. To pass those requirements you probably need entire research group and a lot of trial and error. 1 Million is significant underestimation here if you don’t know requirements.

I started also this journey in about 2 months ago.

Was looking for some peer on learning this beautiful beasty.

I think this kind of learning is more effective than solo learning. This way we define some problems and try to find solutions and ideas for them each other. That's very better I would say than doing these things alone.

I'm currently learning based on Hands On ML book chapters.

Wanna join me?

All you need to know is git clone

people saying this might cost a million dollars ist overshot in my opinion - if you have a dataset with at least 10k houses (10x that would be optimal) and the document stays the same i‘d say youre looking at around 30 to 100 k dollars

Giving an estimate without knowing the quality of data and the quantity of data seems impossible, but it could go anywhere from about 10k to more than a million depending on what is expected and with what one would work

Stay strong 🙏🙏

There are a lot of unknowns in your description.

1. Is document format always the same and can you simply extract features from document?
2. What is the maximum error you can afford
3. What is your computational resources? Depending on that price of the final work may vary from couple grand to hundreds of million dollars.

Haha nice, I was looking at your latent disentanglement paper recently. Do you have any ideas for downstream tasks related to latent disentanglement? Not entirely sure where to apply my dissertation

I think that this wont be possible, sry man.

Use any VAD models?

I've been trying to get to lucid Sonic dreams running again it's been a real pain in the butt I used to run on tensorflow and I found a pie torch version but it's still giving me erroring and versioning issues Any assistance or links to notebooks you guys can offer would be really appreciated