Go to grad school, get really good at optimization, prob/stats, linear algebra, and take plenty ML. Masters usually is minimum for ML positions, but PhDs will dominate positions for any cutting edge research

If u were to use gpt 3.5 turbo just wait for 4 before you spend $600 on compute costs

You should do some reading on what game theory is too. It will be necessary to have a general approach for the harder games

There is no game theory function. I’m confused on how you landed to this line of thinking. Try out league-play reinforcement learning strategies here on a simple game like tic tac toe. It should play enough games to learn the best move in every scenario.

What do you think game theory means? These are all game theory based and you can use DL on any of it. Tic tac toe is simple enough to be an explicit algorithm but you can practice making simulations and use DRL for it

Remember, Chess AI’s were the first thing really. Dota/League is significantly harder than chess. So try making AI’s for:

Tic Tac Toe, Simple Dice Game (Farkle seems like a good one), Card game with slightly more intricacies, Checkers, Chess, A simple game involving player movement, League.

Make a deep RL AI on a simple card. Start there, create the card game environment in your own code so you don’t have to worry about api’s.

This. We have no context of what ML even entails here. It’s too broad.

You’re still not asking a clear question. Using ML to build a product or a model being the product. If the model is the product, then your answer is “What’s the difference between an non-DL ML model and a DL model”.

Pytorch / Keras / Tensorflow for deep learning

And any basic ML library you want, scitkit leaen etc.

Deep learning is all about GPU usage and running long experiments in production. I’m confused what you even want

Is the question basically asking, what skills would someone specialized in DL have vs someone specializing in non-DL ML have?

Showing you can create a smaller model with the same performance means SWIN is overparameterized for that given task. Give it datasets with varying complexity, not just one single one.

Google difference between ML and Deep Learning.

Is this a shit post? these are trained on real human faces. Humans look very different than cartoons.

It is.

? You are proving your SWIN model is overparameterized for CIFAR. Make an EVEN simpler model than those, you prob won’t be able to with off the shelf distillation. Doing this just for ImageNet literally doesn’t change anything. It’s just a different more complex dataset.

What’s your end goal? To come up with a distillation technique to make NN’s more efficient and smaller?

MNIST and Imagenrt is a huge range. Try something in between, preferably multiple. For example CIFAR-10 and CIFAR-100. I would expect it to perform more similarly to the full SWIN model on citar-10 because less data complexity.

Are you even retraining these models on cartoon faces?

? Not sure. Train it longer, lower learning rate, are u using teacher forcing? I’m not very familiar with best LSTM practices.

What’s your problem? Normalized data is good.

You are misinterpreting what “normalizing” is. It converts your data to fit a standard normal distribution. That means, you have positive and negative numbers centered around 0. This is optimal for most deep learning models. The interval [0,1] is not good because you want some weights to be negative as certain features negatively impact certain results.

“It’s not yet patented” this sounds so ridiculously funny to me. Publish, progress research, be open to critics on your ideas, without you are just making backless claims. All I see is a hs student who has coded up his little ml algo and thinks it’s agi.

Why am I wasting my time entertaining this

Lmao this is too funny. I am sure you can easily outperform sota models “speed”, but does it have higher performance/accuracy. We use these overparameterized deep models to perform better, not be accurate. How do you know you can perform “as well as a human”? What tests are you running? What is the backbone of this algo. I think you have just made a small neural net and saying “look how fast this is”, but performs soooo much worse in comparison to actually big models. I am taking all of this with a grain of salt because you are in highschool and have no actual judgement of what sota models actually do

“70+ algorithms in the past year” is that supposed to be impressive? Are you suggesting the amount of algorithms you produce have any indicator of how they perform. How do you even tune 70 models in a year.

I have a challenge for you. Since you are in HS, read as much research as you can (probably on efficient networks or whatever you seem to like) and write a review paper of some small niche subject. Then start coming up with novel ideas for it, test it, tune it, push benchmarks and have as many legitimate comparisons to real world models. Then publish it.

Ok so your just spouting bs about agi and have nothing to back up your claims

I’m hoping you at least published in top conferences?

“I have almost figured out an algorithm for an AGI” lmao no you have not. you’re in high school claiming you are the closest person to solving agi rn as a “AI researcher”