Generative AI Tutorials

Generative AI - Code Sample for GRU

Code Sample for GRU

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

# Create a GRU model
model = Sequential()
model.add(GRU(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()

For predicting sequences, this code sets up a GRU model. With 50 units and return_sequences=True, the GRU layer shows the secret state at every time step. Mean squared error loss and the Adam planner are used to put together the model.

You will learn a lot about sequence modeling if you understand and use these three types of recurrent networks: RNNs, LSTMs, and GRUs. Each type is good at something different: RNNs are good because they are simple, LSTMs are good because they can find long-term relationships, and GRUs are good because they use little computing power. These models are very important for many uses, like language modeling, predicting time series, and recognizing speech.

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Generative AI

Beginner 5 Hours

Code Sample for GRU

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

# Create a GRU model
model = Sequential()
model.add(GRU(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()

For predicting sequences, this code sets up a GRU model. With 50 units and return_sequences=True, the GRU layer shows the secret state at every time step. Mean squared error loss and the Adam planner are used to put together the model.

You will learn a lot about sequence modeling if you understand and use these three types of recurrent networks: RNNs, LSTMs, and GRUs. Each type is good at something different: RNNs are good because they are simple, LSTMs are good because they can find long-term relationships, and GRUs are good because they use little computing power. These models are very important for many uses, like language modeling, predicting time series, and recognizing speech.

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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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