You must've had some poorly optimized models then, as even the 40 GB A100 is roughly 2.0-2.1x faster than a 3090, while a 4090 is at most 1.9x but on average 1.5x faster than a 3090 according to various DL benchmarks.

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This. I have the same setup of laptop + work station. It's been great. No model training kills my laptop anymore. More memory for Chrome.

Math behind diffusion, I don't even remember the math.

Dunno why downvote. Is some cloud compute in the budget?

Can you get at the RAM? If so, you can upgrade it relatively easily (and cheaply). Most laptops really need 16 to 32 GB anyway to run efficiently.

Rtx 30070 or waiit for 4070. Then a laptop with good cooling system. Usually a gaming laptop with many holes in the botton and two fans inside , that put air out in the front or sides of the laptop. Then, make linux OS drive at the second SSd slot and have 32 GB or more RAM.

Acl, emnlp

any specific conference?

Read papers

Having owned a 4090 and used many A100s, I can safely say that the 4090 is by far faster than an A100.

Don’t bother, ask department for cluster resources, none of these will be enough for actual fast DL works.

Keep the laptop, buy a used gaming desktop with a 3060 12GB VRAM and ssh into it from your laptop.

I have a msi ge66 raider my work got me, it’s 32gb ram rtx3070 8gb, 1TB SSD. I use it for PyTorch and when I max it out I just use runpod or lambda labs to rent a rtx3090 or a100 for dirt cheap. Would recommend it

Wrong i have a macbook pro 14 m1 and it s faster than my 3080

Unless you have money for an actual workstation like the top end Dell Precisions, Thinkpads or Razer Blades, you should probably not get a laptop to do deep learning on. Those macs will not be that much faster than running stuff on CPU, even if you do get some Metal API to run on it.

Neural networks are just some complex formulas. Not that it involves some complex and fancy mathing it is just multiplication and addition with applied non-linearity. I would suggest making some simple neural networks for like digit recognition using MNIST database. After you understand all the maths than you can go deeper.

Start with supervised learning. Then move to deep RL with cart pole or. something. Thennnnn Dota 2

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

I don't know maybe translation bad but since game theory is maths it have formula no? Nvm, I will try the tic tac toe one. Thank you.

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.

Ima gone try it, but from what I saw on ytb, the neural network use activation function, etc... I don't know where I can put game theory function or formula.

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

For chess and tic tac toe the AI isn't deep learning, it is game theory based behavior no?

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.