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About UNIT-2 Foundations of Deep Learning
The document discusses several topics related to deep networks including: 1. The vanishing gradients problem that occurs when training deep neural networks using certain activation functions like sigmoid. As more layers are added, the gradients approach zero making training difficult. 2. Exploding gradients, which is the opposite problem where gradients accumulate and become very large during training, causing unstable and overflowing weights. 3. Hyperparameters that must be tuned for deep network training, including layer sizes, learning rates, regularization techniques, initialization strategies, and more. 4. Solutions to both the vanishing and exploding gradient problems like using different activation functions and gradient clipping.
Detailed Information
| Author: | ['bhavana'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 64 |
| Language: | English |
| Format: | |
| Price: | FREE |
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