🎓 TensorFlow NLP Tutorial: Text Processing with Keras 🤖

Welcome to the Natural Language Processing (NLP) tutorial using TensorFlow! This notebook guides you through building text classification models with Keras. Let's dive into the world of text data analysis! 📜

📌 Key Topics Covered

• Text Vectorization 📊
  Convert raw text into numerical representations using TextVectorization.
• Model Building 🧱
  Construct a simple neural network for sentiment analysis.
• Training & Evaluation 🔄
  Train the model on the IMDB dataset and evaluate its performance.

🚀 Example Code Snippet

from tensorflow.keras.layers import TextVectorization

vectorize_layer = TextVectorization(max_tokens=1000)
vectorize_layer.adapt(train_dataset)

📌 Step-by-Step Guide

1. Data Preprocessing 🧼
   Use TextVectorization to standardize and tokenize text data.
   Learn more about text preprocessing

2. Model Architecture 🏗️
   Build a sequential model with Embedding and Dense layers.
   

   

   Text Processing Pipeline

3. Training Loop 🔄
   Compile the model with Adam optimizer and SparseCategoricalCrossentropy loss.
   📈 Tip: Monitor validation accuracy to avoid overfitting!

🌐 Expand Your Knowledge

• TensorFlow Text Guide for advanced techniques
• Keras Text Classification Examples
• TFX Pipeline Overview

📌 Final Thoughts

NLP opens doors to analyzing text data with deep learning! 🌍
Whether you're working on sentiment analysis or language modeling, TensorFlow provides powerful tools.
Explore more NLP tutorials to master text processing!