Description:

This course focuses on implementing a Many-to-One LSTM architecture for sentiment analysis and text generation tasks using Python and TensorFlow. You will learn how to preprocess textual data, build LSTM models, train them on sentiment analysis datasets, and generate text based on the learned patterns. By the end of this course, you will have a strong understanding of LSTM networks and how they can be applied to natural language processing tasks.

Key Highlights:

• Implement Many-to-One LSTM architecture
• Perform sentiment analysis on textual data
• Generate text using LSTM models

What you will learn:

• Implement Many-to-One LSTM architecture
  Learn to design and build Many-to-One LSTM models for sentiment analysis and text generation tasks.
• Perform sentiment analysis on textual data
  Understand how to preprocess textual data, create word embeddings, and classify sentiments using LSTM networks.
• Generate text using LSTM models
  Explore the process of generating text sequences with LSTM models and fine-tuning for improved results.