Deep Learning with PyTorch

IBM develops neural-network techniques from classification and shallow networks into deeper architectures and convolutional models. Optimization topics include dropout and normalization, followed by a final project. It suits learners who understand basic Python and want to connect mathematical ideas with practical PyTorch training.

What you will learn

• Build classification and shallow neural-network models.
• Apply techniques for optimizing deeper networks.
• Train and evaluate convolutional models in PyTorch.

Who this suits

Learners with basic Python experience and familiarity with gradients and matrices, following the provider’s requirements.

Before you enroll

Check the Python, PyTorch and hardware versions used in the labs before enrolling. GPU access may have separate costs. Keep training and evaluation data separate, record configurations and compare a model with a simple baseline before attributing a result to a more complex architecture.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Train a small model on a permitted dataset and change one optimization setting at a time, recording validation behavior as well as training loss.

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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