PyTorch Tutorial
Learn Deep Learning with PyTorch
Master PyTorch for deep learning with tensors, autograd, datasets, dataloaders, training loops, CNNs, transfer learning, optimization, debugging, and deployment.
13Topics
35+Examples
FreeAlways
About PyTorch
Master PyTorch for deep learning with tensors, autograd, datasets, dataloaders, training loops, CNNs, transfer learning, optimization, debugging, and deployment.
Prerequisites
Basic computer knowledge is enough to start. Prior programming experience is helpful but not required.
Audience
Designed for beginners, students, interview preparation, and developers who want a clear PyTorch path.
What You'll Learn
Core concepts, examples, common mistakes, practical patterns, FAQs, and a step-by-step learning roadmap.
Tools Needed
Use a modern browser, code editor, terminal, and the available online compiler when supported.
PyTorch Learning Path
Build reproducible PyTorch training and inference pipelines with correct tensors, data, evaluation, checkpoints, and deployment evidence.
- Master tensors, autograd, modules, optimizers, and data
- Train, evaluate, tune, checkpoint, and debug models
- Profile, reproduce, export, serve, and monitor inference
Reproducible Image Classifier
Build an image classifier with deterministic splits, transforms, metrics, checkpoints, error analysis, export, and an inference endpoint.
Milestones
- Create data and model pipeline with seeded runs
- Train, validate, checkpoint, and analyze errors
- Export and test latency, input validation, and drift signals
Completion Evidence
- Experiment record
- Confusion analysis
- Inference test
Open the complete project guide
Reproducible Image Classifier Readiness Check
Complete each criterion and retain the listed evidence before marking the course capstone ready for review.
0 of 6 criteria complete
Self-assessment
Complete every milestone and evidence item to pass this readiness gate.
Tutorial Topics
Follow the lessons in order, or jump straight into the topic you need.
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Open this lesson in the PyTorch tutorial path.
3. Tensors & Autograd
Lesson
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4. Modules, Loss & Optimizers
Lesson
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5. Datasets & DataLoaders
Lesson
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7. CNN & Transfer Learning
Lesson
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8. Regularization & Schedulers
Lesson
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9. Performance & AMP
Lesson
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10. Sequence Models
Lesson
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11. Optimization & Debugging
Lesson
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12. Saving & Deployment
Lesson
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13. Capstone Project
Lesson
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PyTorch Topic Hub
Use the full topic workspace when you want tutorials, practice, interview prep, and nearby topic links in one place.
Open PyTorch topic hub
Tutorials, practice, and prep together
Topic hub
4 surfaces
Tensors and autograd
Modules and data
Learn tensors, automatic differentiation, modules, data pipelines, training loops, optimization, performance, debugging, and deployment in one practical path.
PyTorch Benefits
- Build a strong foundation with clear explanations and examples.
- Practice concepts in a structured order instead of jumping randomly.
- Prepare for interviews with common mistakes, FAQs, and practical notes.
- Learn patterns that transfer to real projects and production work.
- Use one complete learning path from basics to advanced topics.