Deep Learning wit PyTorch

Where to Move

Move chapter:

• ☰ 1 Orientation and Learning Setup
  • ☰ 1.1 Course Goals and How to Use This Outline
  • ☰ 1.2 What You Need to Know Before Starting
  • ☰ 1.3 Installing Python and PyTorch
  • ☰ 1.4 Choosing CPU vs CUDA vs Apple Silicon
  • ☰ 1.5 Using Jupyter, VS Code, and Scripts
  • ☰ 1.6 Reproducibility, Seeds, and Determinism
• ☰ 2 Python and PyTorch Fundamentals
  • ☰ 2.1 Tensors and Basic Operations
  • ☰ 2.2 Tensor Shapes, Dtypes, and Devices
  • ☰ 2.3 Indexing, Slicing, and Broadcasting
  • ☰ 2.4 Autograd and Computational Graphs
  • ☰ 2.5 Common Tensor Pitfalls and Debugging
  • ☰ 2.6 Saving and Loading Tensors
• ☰ 3 Neural Network Building Blocks
  • ☰ 3.1 Linear Layers and Affine Transforms
  • ☰ 3.2 Activation Functions
  • ☰ 3.3 Loss Functions
  • ☰ 3.4 Optimizers and Learning Rates
  • ☰ 3.5 Regularization Basics
  • ☰ 3.6 Weight Initialization Concepts
• ☰ 4 First Models End to End
  • ☰ 4.1 A Simple Regression Model
  • ☰ 4.2 A Simple Classification Model
  • ☰ 4.3 Data Splitting and Validation Sets
  • ☰ 4.4 Training Loop Structure
  • ☰ 4.5 Tracking Loss and Metrics
  • ☰ 4.6 Basic Overfitting and Underfitting Checks
• ☰ 5 Data Handling with PyTorch
  • ☰ 5.1 Datasets and DataLoaders
  • ☰ 5.2 Transforms and Preprocessing
  • ☰ 5.3 Collate Functions and Batching
  • ☰ 5.4 Shuffling, Sampling, and Class Imbalance
  • ☰ 5.5 Working with Tabular, Text, and Image Data
  • ☰ 5.6 Data Pipeline Performance Basics
• ☰ 6 Training Loops in Practice
  • ☰ 6.1 Forward Pass and Backward Pass
  • ☰ 6.2 Gradient Zeroing and Accumulation
  • ☰ 6.3 Device Placement and Mixed Precision Basics
  • ☰ 6.4 Learning Rate Schedules
  • ☰ 6.5 Gradient Clipping
  • ☰ 6.6 Logging with TensorBoard
• ☰ 7 Debugging and Model Troubleshooting
  • ☰ 7.1 Verifying Shapes and NaNs
  • ☰ 7.2 Checking Gradients and Vanishing Gradients
  • ☰ 7.3 Sanity Checks and Overfit a Small Batch
  • ☰ 7.4 Common Causes of Training Instability
  • ☰ 7.5 Interpreting Learning Curves
  • ☰ 7.6 Practical Tips for Faster Iteration
• ☰ 8 Feedforward Networks for Structured Data
  • ☰ 8.1 Feature Scaling and Normalization
  • ☰ 8.2 Embeddings for Categorical Features
  • ☰ 8.3 Multi-Layer Perceptrons in PyTorch
  • ☰ 8.4 Choosing Hidden Sizes and Depth
  • ☰ 8.5 Evaluation Metrics for Classification and Regression
  • ☰ 8.6 Model Export and Inference Basics
• ☰ 9 Convolutional Neural Networks for Images
  • ☰ 9.1 Convolutions, Padding, and Stride
  • ☰ 9.2 Pooling and Feature Maps
  • ☰ 9.3 Building a Basic CNN in PyTorch
  • ☰ 9.4 Data Augmentation for Vision
  • ☰ 9.5 Transfer Learning with Pretrained Models
  • ☰ 9.6 Fine-Tuning and Freezing Layers
• ☰ 10 Recurrent and Sequence Models
  • ☰ 10.1 Sequences, Tokens, and Padding
  • ☰ 10.2 RNNs, LSTMs, and GRUs
  • ☰ 10.3 Handling Variable-Length Sequences
  • ☰ 10.4 Teacher Forcing and Sequence Losses
  • ☰ 10.5 Basic Text Classification Pipeline
  • ☰ 10.6 Simple Sequence-to-Sequence Overview
• ☰ 11 Attention and Transformers Fundamentals
  • ☰ 11.1 Why Attention Helps
  • ☰ 11.2 Self-Attention Intuition
  • ☰ 11.3 Transformer Building Blocks
  • ☰ 11.4 Using Transformer Models in PyTorch
  • ☰ 11.5 Fine-Tuning for Text Classification
  • ☰ 11.6 Managing Tokenizers and Batching
• ☰ 12 Model Evaluation and Experiment Management
  • ☰ 12.1 Train, Validation, and Test Best Practices
  • ☰ 12.2 Cross-Validation Basics
  • ☰ 12.3 Calibration and Thresholding
  • ☰ 12.4 Error Analysis and Confusion Matrices
  • ☰ 12.5 Experiment Tracking Concepts
  • ☰ 12.6 Comparing Models Fairly
• ☰ 13 Saving, Loading, and Deployment Readiness
  • ☰ 13.1 Saving Model Weights and Full Checkpoints
  • ☰ 13.2 Loading for Inference and Resuming Training
  • ☰ 13.3 TorchScript and Tracing Basics
  • ☰ 13.4 Exporting to ONNX Overview
  • ☰ 13.5 Batch Inference and Latency Considerations
  • ☰ 13.6 Reproducible Packaging of Code and Weights
• ☰ 14 Performance and Scaling Basics
  • ☰ 14.1 Profiling and Bottleneck Identification
  • ☰ 14.2 DataLoader Optimization
  • ☰ 14.3 GPU Utilization and Memory Management
  • ☰ 14.4 Gradient Accumulation for Large Batches
  • ☰ 14.5 Distributed Training Concepts Overview
  • ☰ 14.6 Practical Guidelines for Faster Training
• ☰ 15 Best Practices and Next Steps
  • ☰ 15.1 Choosing Hyperparameters Systematically
  • ☰ 15.2 Regularization and Generalization Toolkit
  • ☰ 15.3 Responsible Use and Dataset Considerations
  • ☰ 15.4 Reading PyTorch Documentation Effectively
  • ☰ 15.5 Suggested Projects for Beginners
  • ☰ 15.6 Pathways to Advanced Topics