Navigating Non-Differentiable Loss in Deep Learning: Practical Approaches
Published:Nov 4, 2019 13:11
•1 min read
•Hacker News
Analysis
The article likely explores challenges and solutions when using deep learning models with loss functions that are not differentiable. It's crucial for researchers and practitioners, as non-differentiable losses are prevalent in various real-world scenarios.
Key Takeaways
- •Addresses challenges in applying deep learning where standard gradient-based optimization fails.
- •Discusses methods for dealing with non-differentiable loss like reinforcement learning or derivative approximation.
- •Offers practical insight for practitioners building deep learning solutions in real-world situations.
Reference
“The article's main focus is likely on addressing the difficulties arising from the use of non-differentiable loss functions in deep learning.”