Customising your models with TensorFlow 2

Imperial College London extends TensorFlow development through functional modeling and data pipelines. Sequence models lead into subclassing and custom training loops, followed by a capstone. This is a continuation for learners ready to control model structure and training more directly.

What you will learn

• Build a model with the Keras functional API.
• Organize a data pipeline for training.
• Use subclassing and custom loops in a model project.

Who this suits

Intermediate learners with Python 3 and knowledge of general machine learning and deep learning.

Before you enroll

Check the TensorFlow and Keras versions used by the notebooks. APIs and model formats can change; reproduce a small example before investing time in a larger project.

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Put it into practice

Refactor a small sequential model into a functional or subclassed version. Compare the outputs and explain what extra control the new representation provides.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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