PyTorch Learning Roadmap
A structured path from tensor basics to training and deploying custom neural networks at scale.
Why PyTorch
PyTorch powers most frontier AI research. HuggingFace, PyTorch Lightning, and virtually every new model architecture papers release code in PyTorch. Understanding how to build, train, debug, and deploy PyTorch models is essential for any deep learning role.
Stage 1: Tensors and Autograd (Week 1)
Goal: Understand the computational graph, gradients, and how PyTorch differs from NumPy.
Topics:
- Introduction to PyTorch — tensors, autograd, computational graph, device management
- Neural Networks — nn.Module, layers, forward pass, training loop
Milestone: Implement logistic regression as an nn.Module. Write the full training loop (forward, loss, backward, step) without using any high-level abstractions. Verify gradients are computed correctly by manually checking one step.
Stage 2: Computer Vision (Weeks 2-3)
Goal: Build and train CNNs for image classification.
Topics:
- Convolutional Neural Networks — Conv2d, BatchNorm, MaxPool, residual connections
- Training Best Practices — learning rate scheduling, mixed precision, gradient clipping, checkpointing
Milestone: Fine-tune a pre-trained ResNet-18 on a 5-class custom dataset. Achieve validation accuracy > 90% using learning rate warmup and cosine annealing. Export the model to ONNX.
Stage 3: Sequence Models (Week 3)
Goal: Model sequential and temporal data.
Topics:
- RNNs and LSTMs — LSTM shapes, BiLSTM, packed sequences, time-series forecasting
Milestone: Build a BiLSTM text classifier. Handle variable-length sequences correctly with pack_padded_sequence. Compare against a simple bag-of-words baseline.
Stage 4: Transformers and NLP (Week 4)
Goal: Build and fine-tune transformer models.
Topics:
- Transformers — multi-head attention from scratch, HuggingFace fine-tuning
Milestone: Fine-tune a BERT model for a 4-class text classification task using HuggingFace. Implement warmup LR schedule and mixed precision. Compare to BiLSTM from Stage 3.
Stage 5: Production and Scale (Weeks 5-6)
Goal: Deploy models and train efficiently at scale.
Topics:
- Deployment — ONNX export, TorchScript, ONNX Runtime serving
- Distributed Training — DataParallel, DDP, gradient checkpointing
- Custom Datasets — Dataset, DataLoader, WeightedRandomSampler, IterableDataset
Milestone: Convert a trained model to ONNX and serve it via a thread-safe ONNXModelServer. Benchmark throughput with concurrent requests. Compare ONNX Runtime vs. PyTorch inference latency.
Stage 6: Projects
Beginner: MNIST classifier, image denoiser, sentiment classifier
Intermediate: Object detection from scratch, contrastive learning (SimCLR), transformer language model
Advanced: Custom CUDA kernel, model pruning pipeline, Neural Architecture Search
See PyTorch Projects for full descriptions.
What Comes After PyTorch
- HuggingFace — pre-trained models, fine-tuning, datasets
- PyTorch Lightning — training boilerplate reduction
- MLOps — deploying and monitoring models in production
- Triton / CUDA — custom kernels for maximum performance
Resources
- PyTorch official tutorials: pytorch.org/tutorials
- “Deep Learning with PyTorch” by Eli Stevens et al.
- fast.ai deep learning course (PyTorch-based, code-first)
- HuggingFace course: huggingface.co/learn