Generative AI - Understanding LSTMs

Understanding LSTMs

A special type of RNN called Long Short-Term Memory (LSTM) networks can learn long-term relationships. Their method for solving the vanishing gradient problem is unique. It has three gates: the input gate, the forget gate, and the output gate. These gates control how information enters and leaves the cell state, which is a memory that stores important data for long periods of time. The forget gate decides what information to throw away, the input gate decides what information to add to the cell state, and the output gate decides what information from the cell state is used in this time step. LSTMs can keep a stable gradient and better understand long-term relationships than regular RNNs because of this structure.

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Generative AI

Beginner 5 Hours

Understanding LSTMs

A special type of RNN called Long Short-Term Memory (LSTM) networks can learn long-term relationships. Their method for solving the vanishing gradient problem is unique. It has three gates: the input gate, the forget gate, and the output gate. These gates control how information enters and leaves the cell state, which is a memory that stores important data for long periods of time. The forget gate decides what information to throw away, the input gate decides what information to add to the cell state, and the output gate decides what information from the cell state is used in this time step. LSTMs can keep a stable gradient and better understand long-term relationships than regular RNNs because of this structure.

Frequently Asked Questions for Generative AI

Sequence of prompts stored as linked records or documents.

It helps with filtering, categorization, and evaluating generated outputs.



As text fields, often with associated metadata and response outputs.

Combines keyword and vector-based search for improved result relevance.

Yes, for storing structured prompt-response pairs or evaluation data.

Combines database search with generation to improve accuracy and grounding.

Using encryption, anonymization, and role-based access control.

Using tools like DVC or MLflow with database or cloud storage.

Databases optimized to store and search high-dimensional embeddings efficiently.

They enable semantic search and similarity-based retrieval for better context.

They provide organized and labeled datasets for supervised trainining.



Track usage patterns, feedback, and model behavior over time.

Enhancing model responses by referencing external, trustworthy data sources.

They store training data and generated outputs for model development and evaluation.

Removing repeated data to reduce bias and improve model generalization.

Yes, using BLOB fields or linking to external model repositories.

With user IDs, timestamps, and quality scores in relational or NoSQL databases.

Using distributed databases, replication, and sharding.

NoSQL or vector databases like Pinecone, Weaviate, or Elasticsearch.

With indexing, metadata tagging, and structured formats for efficient access.

Text, images, audio, and structured data from diverse databases.

Yes, for representing relationships between entities in generated content.

Yes, using structured or document databases with timestamps and session data.

They store synthetic data alongside real data with clear metadata separation.



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