123 points by techguru 2 years ago flag hide 11 comments
deeplearningfan 2 years ago 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 2 years ago 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 2 years ago 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 2 years ago 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 2 years ago 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 2 years 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 2 years ago 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 2 years ago 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 2 years 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 2 years ago next
TensorWrangler, I too am curious how your research goes with these new optimization methods! Best of luck to you.
practicaldl 2 years ago 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?