UDUdemy
PyTorch for Deep Learning Bootcamp
Intermediate 17 hours English Completion Certificate Certificate
About this course
This bootcamp covers PyTorch from the ground up: tensors, autograd, and nn.Module, then advances to CNNs for image classification, recurrent architectures, and transfer learning with pretrained models. Every section is project-driven with production-quality code.
The course mirrors Daniel Bourke's widely-starred GitHub repository, so you finish with a real portfolio of trained models and clean, reusable PyTorch code.
What you'll learn
Build and train neural networks using PyTorch's nn.Module API
Implement CNNs for image classification with custom datasets
Apply transfer learning with pretrained torchvision models
Use DataLoaders for efficient training pipelines
Evaluate and debug model performance systematically
This course includes
17h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
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Instructor
JP
Jose Portilla
Udemy instructor
210K+ learners20 courses4.6 instructor rating
Daniel Bourke is a machine learning engineer and educator known for practical, project-first teaching. His PyTorch tutorial repository has tens of thousands of GitHub stars.
Requirements
- Python proficiency; basic ML concepts helpful but not required
Who this course is for
- Python developers learning deep learning with PyTorch
- Data scientists transitioning from sklearn to neural networks
- ML practitioners who learned TensorFlow and want PyTorch fluency
About this provider
UD
Udemy
The world's largest online learning marketplace. 65M+ students, 210,000+ courses.
Frequently asked questions
Yes — the course covers torch.compile and other 2.x improvements.
Different philosophy — this is bottom-up (tensors first), fast.ai is top-down. Both are excellent; choose based on your learning style.