Navigating Non-Differentiable Loss in Deep Learning: Practical Approaches

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.

• •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.