TensorFlow, Keras, and the Course Setup
help learners understand the TensorFlow/Keras ecosystem and set up a small reliable workflow.
Deep Learning Basics with TensorFlow and Keras
A responsible TensorFlow/Keras portfolio with tensors, data pipelines, model APIs, training, diagnostics, vision, text, export, and a capstone model card.
Tensors, Shapes, and TensorFlow Data
make learners fluent with TensorFlow tensors and input pipelines.
Keras Model APIs
teach the main Keras ways to define models.
Compile, Fit, Metrics, and Validation
make the standard Keras training workflow clear.
Callbacks, Checkpoints, and TensorBoard
teach experiment discipline before larger models.
Regularization and Training Diagnostics
teach why Keras models fail and how to repair them carefully.
Computer Vision with Keras
introduce image classification through small understandable models.
Transfer Learning and Fine-Tuning
show how pretrained Keras models change the workflow.
Text, Tokenization, and Embeddings
introduce text workflows without jumping straight to large language models.
Transformer Workflows and Pretrained Models
teach practical transformer use while keeping claims honest.
Saving, Export, and Responsible Use
teach what a saved model is and what production readiness still requires.
Capstone TensorFlow/Keras Portfolio
bring the course together into a responsible final project.
First-release boundary
This is a text-first course shell. Leonardo/image creation, external review, learner pilot evidence, and production publication remain separate later tasks.