650 points by nn_optimizer 1 year ago flag hide 11 comments
nn_enthusiast 1 year ago next
Fascinating article on Neural Network Optimization Techniques! The increasing demand for faster learning has seen some breakthrough techniques that can be useful for many applications. Kudos to the author.
faster_learning 1 year ago next
@nn_enthusiast Thanks for your kind words. As you mentioned, faster learning is key for many applications and I hope this piece helps demystify adaptive optimization for the HN community.
gradient_descent 1 year ago prev next
There's no way we can discuss optimization techniques without gradients. They're fundamental for any learning algorithm.
adaptive_learning 1 year ago next
@gradient_descent I agree that gradients are essential, but they have limitations. Adaptive optimization algorithms like RMSprop and Adam can help tackle those issues.
optimizer_user 1 year ago next
@adaptive_learning I've been using Adam for a few projects and it has increased training speeds dramatically. Didn't really look into the details of adaptive techniques but this thread is tempting me to do so.
quantization_lover 1 year ago prev next
One technique I've been playing around with recently is weight quantization. It reduces the memory consumption of models and has speed benefits too.
nn_student 1 year ago next
@quantization_lover How have your experiments with quantization affected the model accuracy? I heard mixed reviews on the impact to model quality.
quantization_lover 1 year ago next
@nn_student Overall, I've found the accuracy remains acceptable given the speed improvements. I'm more interested in real-time applications where speed is paramount over model accuracy.
deep_learning_pro 1 year ago prev next
Beyond optimization techniques, techniques like distillation or pruning can have a significant impact on training times. Pruning is helpful since it reduces the model size and you could integrate it with quantization.
on_the_fence 1 year ago next
@deep_learning_pro The problem is that some of these methods are application-specific. Would be nice if there was a more generalized approach to model optimization and not only training speed.
nn_enthusiast 1 year ago prev next
@deep_learning_pro I think you're right about pruning and quantization being interesting additions to the mix. Thank you for these ideas.