Building and Training Neural Networks with PyTorch

Packt applies PyTorch to a range of modeling tasks, including images, audio and object detection. Transfer learning and recurrent networks extend the examples beyond a single classifier. It is a practical continuation for learners who know Python and machine-learning basics and want more varied training projects.

What you will learn

• Train classification models for different data types.
• Apply convolutional and recurrent networks.
• Use pretrained models and transfer learning.

Who this suits

Data scientists, ML engineers and enthusiasts with basic Python and machine-learning knowledge.

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

Adapt a permitted pretrained model to a small sample task and compare its evaluation results with a model trained from the beginning.

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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