Download the working file: https://github.com/laxmimerit/Google-Stock-Price-Prediction-Using-RNN—LSTM
Recurrent Neural Networks can Memorize/remember previous inputs in-memory When a huge set of Sequential data is given to it.
These loops make recurrent neural networks seem kind of mysterious. However, if you think a bit more, it turns out that they aren’t all that different than a normal neural network. A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor.
Different types of Recurrent Neural Networks.
Sequence output (e.g. image captioning takes an image and outputs a sentence of words).
Sequence input (e.g. sentiment analysis where a given sentence is classified as expressing a positive or negative sentiment).
Sequence input and sequence output (e.g. Machine Translation: an RNN reads a sentence in English and then outputs a sentence in French).
Synced sequence input and output (e.g. video classification where we wish to label each frame of the video)
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