Understanding LSTM Networks

 

Understanding LSTM Networks

Lstm Network

Recurrent Neural Networks

Man does not start his thinking from scratch every second. As you read this essay, you understand each word based on your understanding of the previous words. You don't throw everything away and start thinking from scratch again. There is persistence in your thoughts.

Traditional neural networks cannot do this, and this seems like a major drawback. For example, imagine that you want to classify what kind of event is happening at every point in a film. It is unclear how a traditional neural network can use its logic about past events in the film to inform people later.

Recurrent neural networks solve this problem. They are networked with loops in them, thereby maintaining information.
In the diagram above, a part of the neural network, \ (A \), looks at some input \ (x_t \) and outputs a value \ (h_t \). A loop allows information to be passed from the next phase of the network.

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