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Wonder if they have a CEO.
r/cryptocurrency
comment
r/CryptoCurrency
2024-12-14
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I was about to chime in and add some info but this says it all 😂
r/cryptocurrency
comment
r/CryptoCurrency
2024-12-14
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Have you seen the PWR Chain infra? Tech is solid; replicates web2 DevX/UX. Might go on to be the choice infra for web2 devs transitioning to web3.
r/web3
comment
r/web3
2024-12-14
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Use the recovery phrase (that you hopefully saved somewhere)
r/cryptocurrency
comment
r/CryptoCurrency
2024-12-14
Z0FBQUFBQm5YV0MtRGxQTC05eEQyZFN1Y0o5VUpRak9nU2lWWVp5Z0VjUzgyLXB1bUJLdk00QzdoT2JtTk1yRFBfNDZ0amlydWJ4YTQ5XzZ3OFJVRVpvQkYxOVhYMGw0WHc9PQ==
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Probably NFT season is just started and a bunch of NFT collections are already undervalued. For example the historic NFT (before 2018) like Mooncats, Cryptocrystals and... this gem: ETH Town. From 2018 and still with a floor price of 0.006 ETH. It's a royally bargain in my opinion. Take a look into the timeline attached. What do you think about it?
r/nft
post
r/NFT
2024-12-14
Z0FBQUFBQm5YV0Mtb0lWVm5mQWVFZm13ZjJ1SVZuN2g1MF9CZGNqOW1rTFdTUFc0VmZSTU9vY2hJTUdSWE1FUXhJRXA2OS1uZ19EYmRTOVdHc0xvdXhxZFg5MmlnSVFJMmc9PQ==
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Popcorn?
r/cryptocurrency
comment
r/CryptoCurrency
2024-12-14
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Did you just post this without actually digesting it at all? 
r/cryptocurrency
comment
r/CryptoCurrency
2024-12-14
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r/im14AndThisIsDeep
r/cryptocurrency
comment
r/CryptoCurrency
2024-12-14
Z0FBQUFBQm5YV0MtanVWOVA2b0hEZHppUllDd0ZxdnpsblRSMkZBMjJyZXF6b1I2YXI3VS1mNGF1SHl1a19kRU5DMUJoVlFXZi1tZ2taTzZrVVk3czRsdFFOX0RjajFyb0E9PQ==
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This cycle is different, because i already sold, now everything will go up x1000, no need to thanks me guys
r/cryptocurrency
comment
r/CryptoCurrency
2024-12-14
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It's the burgers who dump every day
r/cryptocurrency
comment
r/CryptoCurrency
2024-12-14
Z0FBQUFBQm5YV0Mtd0VmZTM1c2J6T096NExqcHREajl4RTV3dTNaYmo5dTM1aVBCcnBVQTFsUGthSVRJdWVNaFlsSlZZeHJZcmZYdGZ2SUNoT1dQX09KRFN1Ul80UHhqeHc9PQ==
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https://opensea.io/collection/magaoriginals/overview
r/nft
comment
r/NFT
2024-12-14
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Same. Everything has to go in Q3 2025 except BTC.
r/cryptocurrency
comment
r/CryptoCurrency
2024-12-14
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That person thanks you for being exit liquidity
r/cryptocurrency
comment
r/CryptoCurrency
2024-12-14
Z0FBQUFBQm5YV0MtWnpyZmFNWHByQjZ5dDFEZjZoUmRjbWlXTWxtcHJYN0s1ajRaVFlqOTVCakt6dDBkUnhLQUdTYTk4bTBTQ2RVemZwZDU4VHNjYmxhLWFUdzhOZk5UaHc9PQ==
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They list their 2024 predictions that they got right at the start of the article and they seem very good.
r/cryptocurrency
comment
r/CryptoCurrency
2024-12-14
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Disclaimer: I posted this in r/learnmachinelearing first, but the sub seems to be more concerned with very basic questions, courses and hiring, so feel free to remove it if it doesn't fit here (tho I think that also fits this sub as a discussion). I now have a few years of experience building and training different model architectures, I know most of the basic theory and am able to follow most papers. So my question goes into a more methodological direction. While I am able to successfully build models for a number of applications, a lot of the time this is to a large extended guesswork. I try out different stuff and see what sticks. I know there is a lot of research in the direction of interpretability going on, but this is not directly the direction I want to go with this. Instead I want to ask you all what general advice you have on the training process, what are some practical observations, rules of thumb, approaches you take that are not described in a paper or theoretical ml class. For example: - How do you analyze gradients in your model. I know how to do some very basic plots in this regard, but would be interested in your methods and how you read them from a practical perspective? - How do you visualize temporal instabilities between optimizer steps resulting from e.g. a too large learning rate? - How do you determine appropriate regularization? - What are your rules of thumb for diminisheing returns during a training run? - How do you tune your hyperparameters? I eyeballed them more or less and also used optuna for this in the past. - What are some important intuitions, unwritten rules and pitfalls during training in your opinion? - What are your debugging steps when a model does not perform as expected? - What tricks do you actually use? There are lots of small tricks (EMA, obscure activation functions, ...) that promise some gains, but what do you actually use? - How does your approach differ when you do a transformer, CNN, diffusion model, ... - Some general opinions or tips that I might have missed above. University classes and online resources mostly teach the basics or theoretical foundation, which is very important, but in practice only part of the story. Real world experience also helps, but you only get so far with trial and error and might miss something useful. I am aware of the blog posts by Karpathy on the training of neural networks and look for more resources in this direction. I am happy to here your replies on this arguably broad topic.
r/machinelearning
post
r/MachineLearning
2024-12-14
Z0FBQUFBQm5YV0MtZ2o0YjgzQmxhOUtfS0hzMmk0UzRzSVFWVExIdmVrVDRJeERZY3RERnFPN0ZlVEwzOVQyMm5xanpJVGJPMHVFY1J1WTRzenVWSDlVX0VDVjBmY1MwWlE9PQ==
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