RNN basics in deep learning
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Recurrent neural networks (RNNs) are a type of neural network that can handle sequential data by saving the output of each layer and feeding it back as input. RNNs were created to address issues with feed-forward neural networks, which cannot handle sequential data, only consider the current input, and cannot memorize previous inputs. RNNs have applications in areas like image captioning, time series prediction, natural language processing, and machine translation.
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RNN basics in deep learning
- 1. RNN basics in deep learning Prof. Neeraj Bhargava Kapil Chauhan Department of Computer Science School of Engineering & Systems Sciences MDS University, Ajmer
- 2. Introduction A Neural Network consists of different layers connected to each other, working on the structure and function of a human brain. It learns from huge volumes of data and uses complex algorithms to train a neural net.
- 3. Recurrent Neural Network A Recurrent Neural Network works on the principle of saving the output of a particular layer and feeding this back to the input in order to predict the output of the layer.
- 4. Simple Recurrent Neural Network
- 6. Feed-Forward Neural Networks A feed-forward neural network allows information to flow only in the forward direction, from the input nodes, through the hidden layers, and to the output nodes. There are no cycles or loops in the network.
- 8. Why Recurrent Neural Networks? Recurrent neural networks were created because there were a few issues in the feed-forward neural network: Cannot handle sequential data. Considers only the current input. Cannot memorize previous inputs.
- 9. Applications of Recurrent Neural Networks Image Captioning Time Series Prediction Natural Language Processing Machine Translation
- 10. Assignment Explain Recurrent Neural Networks in deep learning with example.