Welcome, data enthusiasts! In this lesson, we will continue our journey into the world of Natural Language Processing (NLP), with an introduction to deep learning for text classification. To harness the power of deep learning, it's important to start with proper data preparation. That's why we will focus today on text preprocessing, shifting from Scikit-learn, which we used previously in this course, to the powerful TensorFlow library.

The goal of this lesson is to leverage TensorFlow for textual data preparation and understand how it differs from the methods we used earlier. We will implement tokenization, convert tokens into sequences, learn how to pad these sequences to a consistent length, and transform categorical labels into integer labels to input into our deep learning model. Let's dive in!

TensorFlow is an open-source library developed by Google, encompassing a comprehensive ecosystem of tools, libraries, and resources that facilitate machine learning and deep learning tasks, including NLP. As with any machine learning task, preprocessing of your data is a key step in NLP as well.

A significant difference between text preprocessing with TensorFlow and using libraries like Scikit-learn, lies in the approach to tokenization and sequence generation. TensorFlow incorporates a highly efficient tokenization process, handling both tokenization and sequence generation within the same library. Let's understand how this process works.