Foundations and Core Concepts of PyTorch

Packt revisits machine-learning and deep-learning concepts before developing tensors and models in PyTorch. Evaluation and a neural network built from scratch connect the theory with the framework. Although the course covers foundations, its stated audience already has Python and machine-learning knowledge.

What you will learn

• Configure a PyTorch development environment.
• Explain tensors and neural-network building blocks.
• Train and evaluate a model from foundational concepts.

Who this suits

Learners with Python and machine-learning foundations; the provider also lists existing PyTorch familiarity in its preparation guidance.

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

Implement a small neural network on permitted sample data and explain how its manual components correspond to PyTorch’s higher-level operations.

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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