Lesson 14 Lstm Prediction Python Machine Learning Algorithm
LSTM (Long Short-Term Memory) is a Recurrent Neural Network (RNN) based architecture that is widely used in natural language processing and time series forecasting.
The LSTM rectifies a huge issue that recurrent neural networks suffer from: short-memory. Using a series of ‘gates,’ each with its own RNN, the LSTM manages to keep, forget or ignore data points based on a probabilistic model.
LSTMs also help solve exploding and vanishing gradient problems. In simple terms, these problems are a result of repeated weight adjustments as a neural network trains. With repeated epochs, gradients become larger or smaller, and with each adjustment, it becomes easier for the network’s gradients to compound in either direction. This compounding either makes the gradients way too large or way too small. While exploding and vanishing gradients are huge downsides of using traditional RNN’s, LSTM architecture severely mitigates these issues.
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