Introduction to Deep Learning & Neural Networks with Keras

IBM moves from neural-network concepts through the foundations of deep learning into Keras and related libraries. A model-development module precedes the final project. The sequence offers an implementation-oriented introduction, while Coursera classifies its expected starting level as intermediate.

What you will learn

• Explain introductory neural-network concepts.
• Use Keras in a deep-learning workflow.
• Develop and assess a model in a final project.

Who this suits

Intermediate learners; detailed prerequisites are not stated in the published course facts. Preview the Python and mathematics expected in the exercises.

Before you enroll

Preview the mathematics, coding framework and exercise environment. Compare results on held-out data and investigate failure cases before deciding that a more complex network is useful.

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Put it into practice

Train a small network and compare its validation performance with a simple baseline. Explain one modeling choice using evidence from the experiment.

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.

View the curriculum and current access options at Coursera.

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