Introduction to Neural Networks and PyTorch

IBM starts with tensors and datasets before developing regression and classification models. The course connects gradient-descent concepts with PyTorch’s way of training a model and ends with a project. It is a foundations option for learners who want to understand the mechanics beneath more advanced neural networks.

What you will learn

• Create and manipulate tensors and datasets.
• Train regression models with gradient descent.
• Apply logistic regression to classification.

Who this suits

Learners exploring neural networks and PyTorch foundations; preview the Python and mathematical exercises to assess preparation needs.

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

Build a fictional regression dataset with a known relationship and compare your model’s predictions with that relationship on a separate test split.

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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