LSTM Architecture.pptx

CNN

1. LSTM Architecture

2. What is LSTM Architecture? The LSTM architecture aims to provide a short-term memory for RNN that can last thousands of time steps , thus "long short-term memory". A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate.

3. The  input gate and forget gate  determine how the next internal state is influenced by the input, and the last internal state respectively. The  output gate  determines how the output of the network is influenced by the internal state. Sometimes, it’s helpful to remember important details but only use them later. LSTM cell:

4. Contribution of LSTM Architecture to Machine Learning Since its introduction in 1997, LSTM architecture has made significant contributions to the field of machine learning. One of its key contributions is its ability to handle long-term dependencies in data sequences, which was previously a major challenge for traditional RNNs. In addition, LSTM architecture has been used in various applications such as speech recognition, language translation, and image captioning. Its ability to capture long-term dependencies has enabled it to achieve state-of-the-art performance in these tasks, making it a valuable tool for researchers and practitioners alike.

5. Examples of LSTM Architecture in Practical Applications LSTM architecture has been used in a wide range of practical applications, including speech recognition, language translation, and image captioning. In speech recognition, LSTM networks are used to model the temporal dynamics of speech signals, enabling them to accurately transcribe spoken words. The LSTM architecture aims to provide a short-term memory for RNN that can last thousands of time steps, thus "long short-term memory". A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate.

6. Conclusion In conclusion, LSTM architecture has revolutionized the field of machine learning by enabling the modelling of long-term dependencies in data sequences. Its ability to capture complex temporal dynamics has made it a valuable tool for a wide range of applications, from speech recognition to image captioning.