Notes on Weight Initialization for Deep Neural Networks
Published:May 20, 2019 19:55
•1 min read
•Hacker News
Analysis
This article likely discusses the importance of proper weight initialization in deep learning to avoid issues like vanishing or exploding gradients. It probably covers different initialization techniques and their impact on model performance. The source, Hacker News, suggests a technical audience.
Key Takeaways
- •Weight initialization is crucial for the successful training of deep neural networks.
- •Different initialization methods exist to address issues like vanishing/exploding gradients.
- •The choice of initialization method can significantly impact model performance and convergence speed.
Reference
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