TensorFlow Developer Specialization · NLP in TensorFlow

IMDB Sentiment with Stacked LSTMs

A binary sentiment classifier for IMDB reviews using subword tokenization and stacked bidirectional LSTM layers.

WordPiece tokenizationBidirectional LSTMreturn_sequencesSentiment analysistf.data pipeline

What this project is about

The stacked LSTM project models movie-review sentiment with recurrent layers. It also demonstrates why intermediate recurrent layers need return_sequences=True when another recurrent layer follows.

Implementation focus

The key learning was architecture depth in RNNs: deeper sequence models are possible, but they train slower and can overfit, so validation curves matter.

Key results and artifacts

Model parameters: 526,017
Epoch 4 validation accuracy: ~85.86%
Epoch 5 showed overfitting signs

What I learned

This milestone clarified why deep recurrent stacks need sequence outputs in intermediate layers.

Milestone deep dive

What this project proves in my learning path.

A binary sentiment classifier for IMDB reviews using subword tokenization and stacked bidirectional LSTM layers. The milestone is included because it shows a complete learning unit: a problem, a data representation, an implementation path, measurable outputs, and a lesson that later connects to larger AI systems.

Input layerordered sequences of text, audio, dates, or musical events

Model layerrecurrent or attention-based sequence model

Evaluation layertests, metrics, plots, or qualitative output

Artifact layerclassification, generation, alignment, or trigger probabilities

Problem framing

What I was trying to solve

This page is not just a lab description. It explains how I transformed the assignment idea into an engineering story: what the model receives, what it optimizes, what output it should produce, and how the result can be inspected.

Implementation

What I coded or assembled

The implementation focus was the mechanism behind the result: functions, model blocks, training logic, preprocessing, evaluation, and the small technical details that make the notebook work rather than only look correct.

Concepts

Core concepts

WordPiece tokenizationBidirectional LSTMreturn_sequencesSentiment analysistf.data pipeline

These tags show the technical ideas this milestone added to my toolkit.

Learning result

What changed after it

This milestone clarified why deep recurrent stacks need sequence outputs in intermediate layers.

Important outputs and evidence

Model parameters: 526,017
Epoch 4 validation accuracy: ~85.86%
Epoch 5 showed overfitting signs

How I would explain it in an interview

I would describe this milestone by starting from the data representation, then explain the model or algorithm, then show what was measured. The important part is not only the final number; it is the ability to reason about why the method works, what assumptions it makes, and what limitations should be improved next.