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Model Development

PyTorch Deep Learning

Complete PyTorch Deep Learning syllabus path — from basic tensor operations and Autograd calculations to GPU CUDA accelerations, CNNs, and recurrent networks.

Master tensor programming, dynamic computation graphs, automatic differentiation, model architectures, custom loaders, and multi-GPU acceleration pipelines.

8Modules
16Parts
1CUDA Setup
Module 1

Tensors & Dynamic Graphs

Master multi-dimensional tensor arrays, dynamic run-time layout compilation, and basic initializations.

  • Tensor declarations & properties
  • Dynamic vs. Static session graphs
  • Define-by-Run control flow loops
Read Module
Module 2

Autograd Engine

Analyze how requires_grad, DAG operations tracking, and backward loops compute network weight derivatives.

  • Gradient tracking flags
  • Vector-Jacobian products DAGs
  • The Backward pass trigger
Read Module
Module 3

torch.nn & Pipelines

Structure models inheriting from nn.Module, specifying active layer transformations, criterion, and SGD updates.

  • Linear and Activation layers
  • Zeroing gradients buffers
  • Standard training step loops
Read Module
Module 4

Dataset & DataLoader

Implement custom index mapping subclasses to preprocess items, handle mini-batch partitions, and assign worker nodes.

  • Custom CSV & Image subclasses
  • Batch size & shuffling configs
  • Parallel workers streams
Read Module
Module 5

GPU CUDA Acceleration

Accelerate matrix logic operations by pushing weights, variables, and tensors to GPU VRAM using CUDA pipelines.

  • CPU vs GPU core counts
  • torch.device config checks
  • PCIe bus data transfers
Read Module
Module 6

Optimizations & Regularization

Minimize loss functions using Adam and SGD. Fine-tune learning rate schedules, weight decay, and dropout masks.

  • Stochastic updates vs Adaptive Adam
  • Dropout node deactivations
  • Batch Normalization layers
Read Module
Module 7

CNNs Classifier Pipelines

Extract structural context from grid arrays. Compute Conv2d channels, kernel boundaries, and MaxPool steps.

  • Convolutional 2D filters
  • MaxPool dimensions transitions
  • Torchvision image transforms
Read Module
Module 8

RNNs & Time Series Sequences

Process sequence context logs through recurring weights states. Implement unrolled backpropagation loops.

  • Hidden recurrent context vectors
  • Backpropagation Through Time DAGs
  • Sequential text & series training
Read Module

PyTorch Syllabus Quick Nav

Tensors Dynamic Graphs Autograd Backward Pass torch.nn Training Loop Dataset DataLoader CPU vs GPU CUDA Devices Optimizers Regularization CNN Layers Image Data RNN Basics Sequence Training