PyTorch

Tensors, autograd, neural network building blocks, CNNs, training loops, transfer learning, distributed training, quantization, and deploying models to production with FastAPI and TorchServe.

FundamentalsTopics 1–11

·What is PyTorch?
·Tensors
·Autograd
·nn.Module
·Linear Layers and Activations
·Loss Functions
·Optimizers
·Training Loop
·Datasets and DataLoaders
·CUDA and GPU
·Saving and Loading Models

Start Fundamentals →

IntermediateTopics 12–23

·Convolutional Neural Networks
·Transfer Learning
·Data Augmentation
·Batch Normalization
·Dropout and Regularization
·Learning Rate Schedulers
·Validation and Early Stopping
·Metrics and Evaluation
·Mixed Precision Training
·TensorBoard
·Custom Datasets
·Custom Loss Functions

Start Intermediate →

AdvancedTopics 24–35

·RNNs: LSTM and GRU
·Attention and Transformers
·Custom Autograd Functions
·Hooks
·Distributed Training: DDP
·Model Parallelism
·Quantization
·Pruning
·ONNX Export
·TorchScript
·Profiling
·Custom C++ Extensions

Start Advanced →

ProductionTopics 36–45

·FastAPI Model Serving
·TorchServe
·Docker and GPU Deployment
·Memory Management
·Inference Optimization
·Batching for Inference
·MLflow Integration
·Model Versioning
·A/B Testing Models
·Production Monitoring

Start Production →