123 points by deeplearner 1 year ago flag hide 7 comments
deeplearningwizard 1 year ago next
Fantastic article! I've been diving deep into neural network optimization lately, and this post really captures the essence of the challenges we face. I love the detailed outline of optimization techniques explored in this piece, such as learning rate scheduling, gradient clipping, and weight decay. I suggest adding more on second-order optimization methods for a more comprehensive view.
neuralnetworkfan 1 year ago next
@DeepLearningWizard Definitely agree that second-order optimization methods are important, especially in large-scale ML models. There's one more technique that I've recently found helpful: mixed-precision training by NVIDIA (https://developer.nvidia.com/mixed-precision-training).
optimizationexpert 1 year ago prev next
@DeepLearningWizard I couldn't agree more! When dealing with highly complex optimization landscapes, second-order methods like Natural Gradient Descent, LBFGS, and others tend to shine, but convergence can be expensive. You might want to discuss these but mention the high computational cost.
algoenthusiast 1 year ago prev next
Great introduction to NN optimization! I implemented my own variant of the Momentum optimizer based on Nesterov's Accelerated Gradient. I compared it to other popular optimizers, and it worked nicely. Maybe you could add that to the list of techniques explored in the post.
datascientistbob 1 year ago prev next
Very insightful article! More researchers should be aware of the importance of neural network optimization. I suggest having a look at the Adam optimizer which has become quite popular due to its adaptive learning rate.
mlnerd 1 year ago prev next
[quote]@DeepLearningWizard I suggest adding more on second-order optimization methods for a more comprehensive view[/quote] I've found the KFAC approximation method for second-order optimization quite effective. It's part of the TensorFlow Optimizers library (https://github.com/tensorflow/model-analysis/tree/master/tensorflow_optimizers) and helps reduce computation and memory complexity.
mathlovingdeveloper 1 year ago prev next
Excellent article! I'd like to point out that there's a recent project by Google Brain aimed at making differential programming more accessible and optimizer-agnostic called TensorFlow Optax (https://github.com/deepmind/optax).