Learn PYTORCH with Real Code Examples

MNIST handwritten digit classification

CIFAR-10 image classification

Sentiment analysis using LSTM

Time series forecasting

Image segmentation with U-Net or Mask R-CNN

Fix shape mismatches between layers

Handle NaN/Inf loss during training

Ensure tensors are on the correct device (CPU/GPU)

Optimize DataLoader performance

Debug gradient computation issues

Unit-test custom layers and models

Validate model predictions on sample inputs

Monitor GPU utilization

Check for overfitting/underfitting

Verify TorchScript/ONNX exports work correctly

TorchScript for production deployment

ONNX export for cross-framework compatibility

Integration with PyTorch Serve for serving APIs

Use TorchLite for mobile devices

Containerized deployment in cloud environments

TorchVision for computer vision models and datasets

TorchText for NLP datasets and preprocessing

TorchAudio for audio tasks

PyTorch Lightning for high-level training pipelines

ONNX for interoperability and deployment

NumPy and Pandas for data processing

OpenCV for image preprocessing

Matplotlib/Seaborn for visualization

HuggingFace Transformers for NLP

CUDA/cuDNN for GPU acceleration

Use pre-trained models where possible

Leverage PyTorch Lightning for structured training

Profile code early

Use proper batching and data pipelines

Prototype small models before scaling

Train a simple feedforward network

Implement CNN for image classification

Build LSTM for sequence modeling

Use mixed precision and GPU acceleration

Deploy a model via TorchScript or ONNX