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About 3 Deeplearning 1and2
The document summarizes key concepts from Lecture 3 on deep learning: 1) Deep learning uses neural networks with many hidden layers between the input and output layers to represent complex functions. More layers allow the network to learn complex patterns from data. 2) Common network types include fully connected feedforward networks with multiple hidden layers. The neurons in each layer are fully connected to the neurons in adjacent layers. 3) Deep learning has achieved state-of-the-art results in many domains due to increased representational power from additional layers as compared to shallow networks. Popular deep models now have over 100 layers.
Detailed Information
| Author: | ['Nikhil Yelamanchili'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 116 |
| Language: | English |
| Format: | |
| Price: | FREE |
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