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About Deep Learning Notes
1. Loss functions in deep learning need to be differentiable so that backpropagation can be used to train neural networks. Common classification loss functions like cross-entropy are used as proxies for metrics like ROC AUC that cannot be directly optimized. 2. Convolutional neural networks use filters that encode specific aspects of the input data, such as the presence of a face. Feature maps represent the response of each filter over the input. 3. In deep learning models, the cost function is minimized by updating the model parameters in the direction of steepest descent according to the gradient, which is calculated using backpropagation. Non-linear activation functions are required for neural networks to learn non-linear relationships
Detailed Information
| Author: | ['Nishanth Jois'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 110 |
| Language: | English |
| Format: | |
| Price: | FREE |
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