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PyTorch

Dynamic neural networks and research with PyTorch framework.

Course Duration: 10 hours​

What You'll Learn​

  • PyTorch tensors and autograd
  • Building neural networks
  • Training loops
  • Data loading with DataLoader
  • GPU acceleration
  • Model saving and deployment

Prerequisites​

  • Deep Learning Fundamentals
  • Python programming

Course Modules​

  1. PyTorch Introduction
  2. Tensors and Operations
  3. Autograd and Gradients
  4. nn.Module
  5. Building Neural Networks
  6. Loss Functions and Optimizers
  7. Training Loops
  8. DataLoader and Datasets
  9. GPU Training (CUDA)
  10. Saving and Loading Models
  11. PyTorch Lightning
  12. Deployment with TorchScript

Code Example​

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return self.fc2(x)