Learn PyTorch
19 expert-rated courses covering PyTorch. Compared by rating, price, difficulty, and job relevance so you can pick the right one.
Demand for PyTorch skills is soaring as the library becomes the de facto standard for deep learning research and production at major tech companies. Professionals with PyTorch expertise can expect a 20-30% salary premium and strong job prospects in fields like computer vision, natural language processing, and predictive analytics.
Key Facts About PyTorch
- 1PyTorch was originally developed by the AI Research team at Facebook (now Meta) and released in 2016.
- 2PyTorch provides a flexible, Pythonic interface for building and training deep neural networks, with GPU acceleration for faster model training.
- 3The library includes a rich ecosystem of pre-built models, datasets, and optimization algorithms that accelerate deep learning development.
- 4PyTorch has over 30,000 contributors on GitHub and is supported by major AI/ML companies like Google, Microsoft, and Amazon.
- 5In 2021, PyTorch surpassed TensorFlow as the most popular deep learning library based on GitHub stars and overall community engagement.
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Top PyTorch Courses
Deep Learning Fundamentals
Free 10-unit course teaching deep learning from fundamentals to practical model training with PyTorch and PyTorch Lightning.
IBM AI Engineering Professional Certificate
Professional certificate covering ML algorithms, deep learning frameworks, and deployment workflows including Spark, Keras, PyTorch, and TensorFlow.
PyTorch for Deep Learning Professional Certificate
Hands-on professional certificate for building, optimizing, and deploying modern PyTorch deep learning systems.
Practical Deep Learning for Coders (2022)
Free practical deep learning course covering real-world applications, deployment, and foundational model building using PyTorch and fastai.
Practical Deep Learning for Coders, v3
Version 3 of the practical deep learning course with cloud GPU setup guidance and project-first instruction.
Become an AI Researcher
Research-oriented introduction spanning foundational math, PyTorch fundamentals, neural networks, and transformer architecture concepts.
Getting Started with Deep Learning
Self-paced course covering deep learning fundamentals, neural network training, and practical model building with hands-on GPU labs.
Introduction to Deep Learning with PyTorch
Build deep learning models with PyTorch. Cover neural network fundamentals, training loops, CNNs, and sequence models.
IBM AI Engineering Professional Certificate (watsonx)
Earn IBM's AI Engineering Professional Certificate. Master deep learning, computer vision, NLP, and model deployment with TensorFlow and PyTorch.
PyTorch Tutorials
Official PyTorch tutorials hub with beginner to advanced guides and recipes.
Learn the Basics
Step-by-step guide to a complete ML workflow in PyTorch, from data to training and saving models.
Learning PyTorch with Examples
Self-contained examples introducing PyTorch tensors and autograd with practical code snippets.
Fundamentals of Deep Learning
Hands-on deep learning course covering CNNs, data augmentation, transfer learning, and model training.
Deep Learning Fundamentals Certification Exam
Optional final exam for Deep Learning Fundamentals with multiple-choice assessment and badge eligibility on passing score.
Advanced Deep Learning with PyTorch
Master advanced deep learning techniques using PyTorch, the preferred framework for research and production ML. Build sophisticated models including attention mechanisms, transformers, and GANs. Ideal for experienced practitioners wanting to advance their deep learning skills.
Deep Neural Networks With PyTorch
Build and train deep neural networks using PyTorch, one of the most popular deep learning frameworks. Learn architecture design, training techniques, and optimization methods for modern AI applications. Master hands-on implementation through practical projects.
PyTorch for Deep Learning
Professional Certificate by DeepLearning.AI covering PyTorch for deep learning, CNNs, transfer learning, and model deployment.
Deep Learning, NLP, and AI Applications
Advanced course covering RNNs, CNNs, transfer learning, NLP with Transformers, and large language model fundamentals.
Deep Learning Engineering
Advanced specialization covering PyTorch, distributed computing, model deployment, Kubernetes, and performance tuning for production deep learning.
Pro Tips for Learning PyTorch
- #1Start with PyTorch's official tutorial series to learn the fundamentals of tensor operations, autograd, and neural network building.
- #2Practice implementing common deep learning models like convolutional networks, recurrent networks, and transformers from scratch in PyTorch.
- #3Explore PyTorch's advanced features like distributed training, quantization, and mobile deployment to gain a competitive edge.
Why Learn PyTorch?
- PyTorch's intuitive syntax and Pythonic design make it an excellent choice for beginners and experienced developers alike.
- As the leading open-source deep learning framework, PyTorch skills are in high demand at top AI research labs and tech companies.
- The library's flexibility and extensive feature set empower data scientists to rapidly experiment with novel deep learning architectures.