TensorFlow Developer Specialization · Introduction to TensorFlow

Happy or Sad Image Classifier

The first TensorFlow/Keras CNN milestone: loading folder-structured images, normalizing pixels, training a small CNN, and stopping with a custom callback.

Keras Sequential APItf.data image datasetBinary classificationCustom callbackCNN basics

GitHub

What this project is about

This TensorFlow milestone is the first complete Keras image pipeline: image folders, dataset loading, normalization, CNN layers, binary output, and a custom callback.

Implementation focus

It gave me a practical TensorFlow starting point: not just building a model, but controlling the training process and stopping when the target accuracy was reached.

Key results and artifacts

Dataset: 80 images, 40 happy and 40 sad
Image size: 150×150×3
Stopped at epoch 9
Final training accuracy: 100%

What I learned

This was the move from theory into a complete TensorFlow image-classification training pipeline.

Milestone deep dive

What this project proves in my learning path.

The first TensorFlow/Keras CNN milestone: loading folder-structured images, normalizing pixels, training a small CNN, and stopping with a custom callback. 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 layercourse dataset, features, labels, or raw input artifacts

Model layerimplemented algorithm or neural network pipeline

Evaluation layertests, metrics, plots, or qualitative output

Artifact layermetrics, plots, predictions, and visual evidence

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

Keras Sequential APItf.data image datasetBinary classificationCustom callbackCNN basics

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

Learning result

What changed after it

This was the move from theory into a complete TensorFlow image-classification training pipeline.

Important outputs and evidence

Dataset: 80 images, 40 happy and 40 sad
Image size: 150×150×3
Stopped at epoch 9
Final training accuracy: 100%

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.