Deep Learning with PyTorch
PyTorch is an open-source machine learning framework that powers researchers and production systems at scale. This series takes you from tensor basics to deploying trained models, covering the essential tools and patterns you need to build, train, and optimize deep learning applications in Python.
PyTorch's dynamic computation graphs and intuitive API make it ideal for both prototyping and production work. Whether you're building your first neural network or scaling to multi-GPU training, this series provides practical, hands-on guidance with real code examples at every step.
Each article builds on the previous one, forming a complete learning path from tensors and automatic differentiation through advanced techniques like transfer learning and model optimization. You'll learn not just the "what" but the "why"—understanding the mechanics behind PyTorch's design choices so you can make informed decisions when building your own models.
By the end of this series, you'll have the skills to implement state-of-the-art architectures, train models on GPUs efficiently, and deploy them confidently into production environments.
Articles in this series
- PyTorch Tensors: Beginner's Guide
- Autograd in PyTorch: Computing Gradients
- Building Neural Networks with PyTorch Modules
- PyTorch Training Loops: Step-by-Step
- DataLoader and Datasets in PyTorch
- GPU Training with PyTorch on CUDA
- Convolutional Neural Networks with PyTorch
- Transfer Learning in PyTorch
- Saving and Loading PyTorch Models
- Debugging and Optimization Tips