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Part IV Summary: Natural Language Processing with TensorFlow

In Part IV, we shift from numeric tensors to language understanding. This section explores how TensorFlow handles text—turning characters and sentences into meaningful vectors that machines can process.

From classic NLP methods like Bag-of-Words and TF-IDF, to advanced architectures like LSTMs and Transformers, this part teaches you to build systems that interpret and generate language.

Here’s what you’ll master across Chapters 21 to 25:

✅ Chapter 21: Text Preprocessing & Tokenization Learn how to clean, tokenize, and vectorize raw text using TensorFlow’s TextVectorization layer and Keras’s Tokenizer API. You’ll prepare sequence data for embedding layers and deep NLP models.

✅ Chapter 22: TF-IDF, Bag-of-Words Representations Explore foundational NLP techniques—count-based and frequency-based vectorization. Use CountVectorizer and TfidfVectorizer from scikit-learn to create interpretable, fast baselines for text classification tasks.

✅ Chapter 23: RNNs & LSTMs Dive into Recurrent Neural Networks and Long Short-Term Memory (LSTM) layers for learning from sequential text. You’ll build a sentiment classifier and understand how memory helps capture temporal context.

✅ Chapter 24: Transformers in TensorFlow (from Scratch) Discover the self-attention mechanism and build a mini Transformer Encoder using pure TensorFlow. This chapter provides an inside-out understanding of what powers BERT, GPT, and modern NLP architectures.

✅ Chapter 25: NLP Projects – Spam Detection, Sentiment Analysis, Autocomplete Apply everything you’ve learned in three mini-projects that span traditional and deep learning workflows. You’ll build and deploy real models that understand, classify, and even complete natural language.

After Part IV, You Will Be Able To:

  • Preprocess text and convert it to model-ready tensors

  • Choose the right representation (TF-IDF, embeddings, etc.) for your task

  • Build sequence models using RNNs, LSTMs, and Transformers

  • Create and deploy end-to-end NLP systems like sentiment analyzers or spam filters

  • Understand the inner workings of attention-based architectures

Part IV turns raw text into signals of meaning—paving the way for intelligent systems that speak, understand, and respond.