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About Deep Leraning Al Vacio
This document summarizes a paper presented at the 2017 International Conference on Intelligent Computing and Control (I2C2) that reviews deep learning techniques for computer vision tasks. It discusses popular deep learning algorithms like convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), and autoencoders. CNNs are well-suited for tasks involving multiple layer training. RNNs can model sequential data like images but are complex to train. RBMs and autoencoders are generative models that learn features from data. The paper provides an overview of these techniques and their applications in computer vision domains like image classification, object detection, and semantic segmentation.
Detailed Information
| Author: | ['Juan Carlos Rojas'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 5 |
| Language: | English |
| Format: | |
| Price: | FREE |
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