PyTorch Developer Certification

Module 1: Introduction to PyTorch

• PyTorch ecosystem
• Deep learning workflow
• Core PyTorch components
• Dynamic computation graphs
• Development environment
• Common applications

Module 2: PyTorch Environment Setup

• Python environment
• PyTorch installation
• Jupyter Notebook
• GPU support
• CUDA concepts
• Development workflow

Module 3: Tensor Fundamentals

• Creating tensors
• Tensor dimensions
• Shapes
• Data types
• Tensor properties
• Device management

Module 4: Tensor Operations

• Arithmetic operations
• Matrix operations
• Indexing
• Slicing
• Reshaping
• Broadcasting

Module 5: GPU Computing with PyTorch

• CPU vs GPU
• CUDA devices
• Moving tensors
• GPU memory
• Device-aware code
• Performance considerations

Module 6: Automatic Differentiation

• Autograd
• Computational graphs
• Gradients
• requires_grad
• Backpropagation
• Gradient management

Module 7: Neural Network Fundamentals

• Artificial neurons
• Layers
• Weights and biases
• Activation functions
• Forward propagation
• Network architecture

Module 8: Building Networks with torch.nn

• nn.Module
• Linear layers
• Sequential models
• Forward methods
• Custom modules
• Model architecture

Module 9: Loss Functions

• Loss concepts
• Mean squared error
• Cross-entropy loss
• Binary classification loss
• Loss selection
• Custom loss concepts

Module 10: Optimizers

• Gradient descent
• SGD
• Adam
• Learning rates
• Weight decay
• Optimizer configuration

Module 11: Training Neural Networks

• Training loops
• Forward pass
• Loss calculation
• Backward pass
• Parameter updates
• Epoch management

Module 12: Model Evaluation

• Validation loops
• Test datasets
• Accuracy
• Precision and recall
• F1 score
• Performance analysis

Module 13: Datasets & DataLoaders

• Dataset class
• DataLoader
• Batching
• Shuffling
• Data iteration
• Custom datasets

Module 14: Data Preprocessing & Augmentation

• Data transformation
• Normalization
• Image transformations
• Data augmentation
• Pipeline composition
• Preprocessing workflows

Module 15: Convolutional Neural Networks

• CNN fundamentals
• Convolution layers
• Filters and kernels
• Pooling
• Feature maps
• CNN architectures

Module 16: Image Classification

• Image datasets
• CNN implementation
• Training classifiers
• Prediction
• Evaluation
• Error analysis

Module 17: Transfer Learning

• Pretrained models
• Feature extraction
• Fine-tuning
• Freezing layers
• Replacing classifiers
• Transfer learning workflows

Module 18: Sequence Models

• Sequential data
• Recurrent neural networks
• Hidden states
• LSTM
• GRU
• Sequence modelling

Module 19: Natural Language Processing with PyTorch

• Text preprocessing
• Tokenization concepts
• Vocabulary
• Embeddings
• Text classification
• NLP pipelines

Module 20: Attention & Transformers

• Attention mechanisms
• Self-attention
• Multi-head attention
• Transformer architecture
• Positional encoding
• Transformer applications

Module 21: Custom Model Architectures

• Custom layers
• Complex networks
• Multiple inputs
• Multiple outputs
• Model composition
• Architecture design

Module 22: Model Regularization

• Overfitting
• Dropout
• Weight decay
• Batch normalization
• Early stopping
• Generalization

Module 23: Learning Rate Optimization

• Learning rate scheduling
• Step schedulers
• Adaptive scheduling
• Warm-up concepts
• Training stability
• Optimization strategies

Module 24: Mixed Precision Training

• Precision concepts
• Automatic mixed precision
• Gradient scaling
• Memory optimization
• Training speed
• GPU efficiency

Module 25: Distributed Training

• Distributed computing
• Data parallelism
• DistributedDataParallel
• Multi-GPU training
• Process management
• Scaling considerations

Module 26: Model Debugging & Profiling

• Tensor debugging
• Gradient issues
• PyTorch profiler
• Performance bottlenecks
• Memory analysis
• Training diagnostics

Module 27: Experiment Tracking & Reproducibility

• Random seeds
• Experiment configuration
• Hyperparameter tracking
• Training metrics
• Model checkpoints
• Reproducible workflows

Module 28: Model Optimization & Export

• Model optimization
• Quantization concepts
• Pruning concepts
• Model compilation
• Model export
• Inference optimization

Module 29: Deployment & Production Inference

• Model serialization
• Loading trained models
• Prediction services
• Batch inference
• Real-time inference
• Model monitoring

Module 30: Practical PyTorch Development Project

• Dataset preparation
• Architecture development
• Model training
• Performance evaluation
• Model optimization
• Deployment workflow