PyTorch, No Tears

Foundations

1. Environment
2. Tensor
3. Autograd

Data

1. Data
2. Datasets and Caching
3. Data Pipelines at Scale
4. Transformation

Training

1. Loss Functions
2. Optimizer
3. Scheduler
4. Learning
5. Training Recipe
6. Early Stopping
7. Class Imbalance
8. Debugging Training
9. Ragged Sequences
10. Multi-Task Learning

Performance and Scaling

1. Training Performance
2. Memory Optimization
3. Profiling
4. Compiled Models
5. Distributed Training

Models, Persistence, and Deployment

1. Models and Convolutional Neural Networks
2. Saving and Loading Models
3. Checkpoint and Resume
4. Model Zoo
5. Parameter-Efficient Finetuning
6. Fine-Tuning Patterns
7. Custom Autograd
8. Quantization
9. Export and Deploy
10. Serving API
11. Inference Batching

Evaluation and Reliability

1. Calibration and Metrics
2. Experiment Tracking
3. Uncertainty
4. Reproducibility
5. Testing PyTorch Code

Text and Language

1. Text Data
2. Embeddings
3. Sequence Classification
4. Transformer Language Model
5. Recurrent Neural Networks

Graphs

1. Graph Data
2. Message Passing
3. Graph Convolution
4. Graph Classification
5. Heterogeneous Graphs
6. PyTorch Geometric

Forecasting and Recommendation

1. Time Series
2. Recommendation Systems

Vision and Generative Models

1. Generative Adversarial Network
2. Neural Transfer
3. Object Detection
4. TensorBoard

PyTorch, No Tears

Index

Index

© Copyright 2019, One-Off Coder. Last updated on Apr 10, 2026, 11:42:18 PM.