LSTM architecture aims to provide RNNs with long short-term memory using a cell state and three gates: the input gate determines how the next internal state is influenced by the input; the forget gate determines how it's influenced by the last internal state; and the output gate determines how the output is influenced by the internal state. LSTM has made significant contributions to machine learning by handling long-term dependencies, enabling state-of-the-art performance on tasks like speech recognition, language translation, and image captioning. It has been applied successfully in these and other areas by modeling temporal dynamics in speech and capturing complex relationships in data sequences.

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.