A Brief Introduction to LSTM Model

Posted By :Manish Kumar |12th June 2022

What is LSTM?

LSTM (Long short-term memory) LSTM is an artificial recurrent Neural Network (RNN) capable of learning order dependence in sequence prediction problems. LSTM has feedback connections in contrast to standard feed-forward neural networks.


What is RNN?

RNN called a Recurrent Neural Network, is a class of Neural Networks. It is the first algorithm that remembers its input, due to internal memory, in other words, RNN uses sequential data and it is a generalization of feed-forward neural networks that has internal memory.


Why do we use LSTM?


Recurrent neural networks RNNs have a long-term dependency issue that LSTM networks were created expressly to address (due to the vanishing gradient problem). LSTMs, in contrast to more conventional feedforward neural networks, feature feedback connections. With this attribute, LSTMs may process whole data sequences, such as time series, without having to treat each data point separately. Instead, they can leverage the information from earlier data points in the sequence to help analyze later data points. Because of this, LSTMs excel at processing data sequences like text, audio, and general time series.



How LSTM Works?

LSTMs utilize several "gates" that regulate how data in a sequence enters, is stored in, and leaves the network. An output gate, an input gate, and a forget gate are the three gates that make up a standard LSTM.

Consider an LSTM cell as visualized in the following diagram imagine moving from left to right.



Consider an LSTM cell

as visualized in the following

diagram imagine moving from 

left to right.




Forget Gate

The first step is the forget gate. In this step, we will determine which pieces of the cell state—the network's long-term memory—are relevant in light of both the prior hidden state and the fresh incoming data.

Input Gate

The input gate and a new memory network are involved in the following step. This step aims to determine what new information should be added to the network's long-term memory (cell state), given the previous hidden state and new input data.

Note: The input gate and the new memory network are both neural networks in and of themselves, and they both accept identical inputs—the old hidden state and the new input data.

Output gate

The output gate decides the new hidden state. We'll employ three factors—the recently updated cell state, the previous hidden state, and the fresh input data—to make this determination.

About Author

Manish Kumar

Manish holds more than 1+ year of industry experience and is working as a Backend developer. He has sound understanding of the latest technologies and has worked on various AI and ML projects, demonstrating his versatility in software Development. He is proficient in several programming languages, including Core Python, and has experience working with databases and Apache. He has expertise in web development using frameworks such as Django and Flask and has contributed to various AWS services, including deployment. He has contributed to several client projects, including Wellsite Dashboard Project and I-infinity project, showcasing his ability to develop solutions that meet the unique needs of businesses and users.

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