Lstm: Learn what LSTM is

Learn what LSTM is, how it works, and why it is useful for sequence prediction tasks. Understand the three gates and the cell state of LSTM , and see how it differs from RNN and bidirectional LSTM . Long Short-Term Memory ( LSTM ) is an enhanced version of the Recurrent Neural Network (RNN) designed by Hochreiter and Schmidhuber. LSTMs can capture long-term dependencies in sequential data making them ideal for tasks like language translation, speech recognition and time series forecasting. Unlike traditional RNNs which use a single hidden state passed through time LSTMs introduce a memory cell that holds information over extended periods addressing the challenge of learning long-term ... What is Long Short-Term Memory ( LSTM )? Long Short-Term Memory ( LSTM ) networks are a type of recurrent neural network (RNN) capable of learning long-term dependencies. They were introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997 and have since become a cornerstone in the field of deep learning for sequential data analysis. LSTMs are particularly useful for tasks where the context or state information is crucial for prediction, such as language modeling, speech recognition, and time ... Learn how LSTM , a type of RNN, can learn long term dependencies and solve sequence learning tasks such as language modeling, speech recognition, and audio analysis. Find out how GPUs can accelerate LSTM training and inference using cuDNN and TensorRT.