Tutorial-31:Dropout layers in neural networks|Deep Learning

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In this video, we break down dropout layers—a powerful regularization technique used to prevent overfitting in deep learning models. Learn how dropout works, why it’s essential for training robust neural networks, and how to implement it effectively in frameworks like TensorFlow and PyTorch.

🔹 What You’ll Learn:
✅ How dropout improves generalization
✅ The intuition behind random deactivation of neurons
✅ Best practices for dropout rate selection

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