Introduction to Deep Learning with PyTorch
DataCamp
Last updated June 30, 2026
A hands-on, code-first introduction to deep learning using PyTorch, one of the most widely used deep-learning libraries. Working in Python, you build your first neural network and write the training loop that teaches it, set up loss functions for both regression and classification, and let PyTorch compute the gradients that drive learning. The second half moves to making models better: tuning hyperparameters, understanding the components of a network, assembling larger architectures, and measuring how well a model performs. You finish able to build, train, and evaluate a neural network in PyTorch and to explain how deep learning differs from classic machine learning. This is a coding course: it assumes you are comfortable in Python.
What you'll learn
- How deep learning differs from classic machine learning
- Building a neural network and writing its training loop in PyTorch
- Loss functions for regression and classification, and how gradients drive learning
- Tuning hyperparameters and assembling larger architectures
- Measuring and improving model performance
Frequently asked questions about Introduction to Deep Learning with PyTorch
Who is Introduction to Deep Learning with PyTorch for?
Developers comfortable with Python who want a practical, hands-on first course in deep learning with PyTorch.
Is Introduction to Deep Learning with PyTorch free?
No — Introduction to Deep Learning with PyTorch is a paid course.
What are the prerequisites for Introduction to Deep Learning with PyTorch?
Intermediate Python, including comfort with NumPy and supervised learning.
Do you need to code for Introduction to Deep Learning with PyTorch?
Yes — Introduction to Deep Learning with PyTorch involves hands-on coding.
Does Introduction to Deep Learning with PyTorch offer a certificate?
Yes. DataCamp Statement of Accomplishment on completion (requires DataCamp Premium).
Why we suggest this course
A solid, practical entry into deep learning for developers who already know some Python and want to work in PyTorch specifically. It is genuinely code-first — you write the training loop yourself rather than watch one — and it covers the full arc from a first network through tuning and evaluation. It expects prior Python, including comfort with NumPy and supervised learning, so it is an intermediate step rather than a first programming course. You can sample the opening chapter before subscribing; the full course and its Statement of Accomplishment are part of DataCamp Premium.