123 points by neural_explorer 1 year ago flag hide 11 comments
hackerx 1 year ago next
Great post, I've been exploring the depths of NN optimization myself! Any tips on dealing with vanishing gradients in very deep NNs?
nn_wizard 1 year ago next
@hackerX I recommend using techniques like weight initialization, gradient clipping, and normalization. Check out the paper 'Understanding the Difficulty of Training Deep Feedforward Neural Networks' for more info!
nn_wizard 1 year ago prev next
@hackerX Sure! I'd be happy to share more about dealing with vanishing gradients in deep NNs. I used a combination of Xavier initialization, gradient clipping, and weight decay techniques to effectively combat the issue.
someuser 1 year ago prev next
How did you approach optimization for large scale problems? Any specific tricks?
hackerx 1 year ago next
@someuser For large scale problems, I've found that using Stochastic Gradient Descent with momentum (SGD) is quite effective. I also used learning rate schedules and early stopping which helped a lot in my case.
another_user 1 year ago prev next
I'm curious about using genetic algorithms in neural network optimization. How did you fit GAs into your journey and what results did you get?
yet_another 1 year ago prev next
I've been using the Adam optimizer and it has been handling the vanishing gradients problems fine. Have you tried it in your project or do you recommend K-FAC and other preconditioned methods?
deep_learner 1 year ago next
@yet_another Yes, I've used Adam and it worked quite well. However, I found that K-FAC and other preconditioned methods sometimes performed better on larger optimization problems where matrix factorization can be computationally more efficient.
newbie 1 year ago prev next
Just started with neural networks and ML in general. Optimization is such a huge challenge in itself. Any advice for someone getting started?
ai_explorer 1 year ago next
@newbie The first step would be to understand the basics of optimization algorithms like Gradient Descent, Momentum, RMSprop, Adagrad, Adadelta, and Adam. You can implement these in Tensorflow, Pytorch or any other DL framework to get hands-on experience. Experimenting with them will help you decide which one suits a particular problem best.