Deep Learning and Reinforcement Learning

IBM offers a broad sequence across neural-network training and several architecture families. Convolutional, recurrent and transfer-learning modules lead into autoencoders, generative models and reinforcement learning. It is useful for comparing methods before selecting a more specialized follow-up.

What you will learn

• Explain backpropagation and optimizer choices.
• Compare convolutional, recurrent and generative approaches.
• Identify the basic components of reinforcement learning.

Who this suits

Intermediate learners; detailed prerequisites are not stated in the published course facts. Preview the expected Python and machine-learning knowledge.

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

Map three example problems to suitable model families. Explain the required data and feedback for each, including why reinforcement learning needs a different evaluation setup.

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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