Generative AI Tutorials

Generative AI - Code Sample for LSTM

Code Sample for LSTM

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# Create an LSTM model
model = Sequential()
model.add(LSTM(50, input_shape=(None, 1), return_sequences=True))
model.add(Dense(1))

# Compile the model
model.compile(optimizer='adam', loss='mse')

# Summary of the model
model.summary()

In this code, an LSTM model for sequence prediction is set up. With 50 units and return_sequences=True, the LSTM layer shows the secret state at each time step. Mean squared error loss and the Adam planner are used to put together the model.

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Code Sample for LSTM

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# Create an LSTM model
model = Sequential()
model.add(LSTM(50, input_shape=(None, 1), return_sequences=True))
model.add(Dense(1))

# Compile the model
model.compile(optimizer='adam', loss='mse')

# Summary of the model
model.summary()

In this code, an LSTM model for sequence prediction is set up. With 50 units and return_sequences=True, the LSTM layer shows the secret state at each time step. Mean squared error loss and the Adam planner are used to put together the model.

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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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