Tutorial_08_Text_and_Sequences

Text and Sequences: TensorFlow and PyTorch Tutorials

This tutorial provides a comprehensive introduction to processing and modeling text data using TensorFlow and PyTorch. Learn how to represent text numerically, train word embeddings, and build sentiment classification models.

Overview

• Text Representation: Strategies to convert text into numeric data.
• Word Embeddings: Dense, trainable vectors representing words in high-dimensional space.
• Embedding Layers:
  • TensorFlow: Keras Embedding layer.
  • PyTorch: nn.Embedding layer.
• Natural Language Processing (NLP):
  • Sentiment classification.
  • Skip-Gram and Negative Sampling models.

Key Topics

1. Text Representation

• One-Hot Encoding:
  • Sparse representation of words.
• Integer Encoding:
  • Assign unique numbers to words.
• Word Embeddings:
  • Efficient and dense representation learned during training.

2. Building and Training Models

• TensorFlow:
  • "Continuous Bag of Words" (CBOW) style sentiment classification model.
  • Key layers: TextVectorization, Embedding, GlobalAveragePooling1D, and Dense.
  • Save and visualize trained embeddings.
• PyTorch:
  • Using nn.Embedding for word embeddings.
  • Techniques to train and visualize word vectors.

3. Advanced NLP Concepts

• Bag of Words (BoW):
  • Binary vectors to indicate word presence in documents.
• Word2Vec:
  • Continuous Bag-of-Words and Skip-Gram models for learning word representations.
• Skip-Gram with Negative Sampling:
  • Predict context words for a target word.

Learning Outcomes

• Understand how to preprocess and represent text data for machine learning.
• Build and train models using TensorFlow and PyTorch embedding layers.
• Explore advanced NLP techniques, including Word2Vec and Skip-Gram models.

Acknowledgments

• François Chollet
• Tensorflow.org
• pytorch.org