Lilly Tech Systems
PyTorch Deep Dive
Master Facebook/Meta's deep learning framework favored by researchers worldwide. From tensor operations and automatic differentiation to building custom models and training loops — learn PyTorch the right way.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
PyTorch philosophy, dynamic computational graphs, and why researchers love it.
2. Tensors & Autograd
Tensor operations, automatic differentiation, GPU acceleration, and gradient computation.
3. Building Models
nn.Module, built-in layers, custom model architectures, and parameter management.
4. Training Loop
Custom training loops, optimizers, learning rate schedulers, and model checkpointing.
5. Computer Vision
torchvision, transforms, pre-trained models, and fine-tuning for image tasks.
6. Best Practices
Performance tuning, distributed training, debugging, and production deployment.
What You'll Learn
By the end of this course, you'll be able to:
Master Tensors & Autograd
Understand PyTorch's tensor system, automatic differentiation, and GPU computing fundamentals.
Build Custom Models
Design neural networks using nn.Module, create custom layers, and manage model parameters.
Write Training Loops
Implement complete training pipelines with validation, checkpointing, and learning rate scheduling.
Fine-Tune Vision Models
Leverage pre-trained models from torchvision and adapt them to your specific image classification tasks.