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Introduction to Long Short Term Memory (LSTM)

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Summary

This article provides an introduction to Long Short Term Memory networks (LSTMs), which are a type of recurrent neural network capable of handling long-term dependencies. It explains the architecture of an LSTM network and how it works, using an example of a sentence with two clauses.

Q&As

What is an LSTM network?
An LSTM network is a special kind of recurrent neural network that is capable of handling long-term dependencies.

What is the difference between an LSTM network and a standard RNN?
The difference between an LSTM network and a standard RNN is that an LSTM network is explicitly designed to avoid long-term dependency problems.

How does an LSTM network work?
An LSTM network works by first choosing whether the information coming from the previous timestamp is to be remembered or forgotten. Next, the cell tries to learn new information from the input. Finally, the cell passes the updated information from the current timestamp to the next timestamp.

What are the three parts of an LSTM cell?
The three parts of an LSTM cell are the forget gate, the input gate, and the output gate.

What is the purpose of each gate in an LSTM cell?
The purpose of the forget gate is to decide whether to keep or forget information from the previous timestamp. The purpose of the input gate is to quantify the importance of new information. The purpose of the output gate is to generate the output for the current timestamp.

AI Comments

👍 This is a great article for anyone who wants to learn about Long Short Term Memory networks. It explains the architecture and working of an LSTM network in a clear and concise manner.

👎 This article is too technical and does not explain the concepts in a way that is easy to understand.

AI Discussion

Me: It's about long short term memory networks.

Friend: What are those?

Me: They're a type of recurrent neural network.

Friend: What's a recurrent neural network?

Me: It's a neural network that can remember information from previous timestamps.

Friend: Why is that useful?

Me: It's useful for tasks like natural language processing, where you need to be able to remember information from the previous sentence in order to understand the current sentence.

Friend: That makes sense.

Action items

Read the blog post linked in the article to learn more about LSTM networks.
Experiment with different types of recurrent neural networks to see which one works best for your data and task.
Use LSTM networks for tasks that require long-term dependencies to be remembered, such as natural language processing tasks.

Technical terms

Long Short Term Memory (LSTM): a type of recurrent neural network that is capable of handling long-term dependencies
RNN: a type of neural network that is used for persistent memory
Vanishing gradient problem: a problem faced by RNNs where long-term dependencies cannot be remembered
LSTM cell: the basic unit of an LSTM network, consisting of three parts: the forget gate, the input gate, and the output gate
Hidden state: the part of an LSTM cell that remembers information from previous timestamps
Cell state: the part of an LSTM cell that carries information from one timestamp to the next
Softmax: a type of activation function that is used to predict the output of a neural network