Deep Learning with PyTorch

Deep learning is a subset of machine learning that uses neural networks to analyze various types of data. PyTorch is an open-source deep learning framework that provides flexibility and speed, making it a popular choice among researchers and developers.

To get started with PyTorch, you need to install it. You can install it via pip:

pip install torch torchvision torchaudio

3.1 Tensors

Tensors are the fundamental building blocks of PyTorch. They are similar to NumPy arrays but can be used on GPUs for accelerated computing.

import torch

# Creating a tensor
x = torch.tensor([[1, 2], [3, 4]])
print(x)
            

3.2 Autograd

PyTorch's autograd feature automatically calculates gradients, which is crucial for training neural networks.

x = torch.ones(2, 2, requires_grad=True)
y = x + 2
print(y.grad_fn)  # Shows the function that created the tensor
            

To build a neural network in PyTorch, you typically define a class that inherits from torch.nn.Module.

import torch.nn as nn

class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(2, 2)

    def forward(self, x):
        return self.fc1(x)
            

Training a model involves passing the data through the model, calculating the loss, and updating the weights using an optimizer.

import torch.optim as optim

model = SimpleNN()
optimizer = optim.SGD(model.parameters(), lr=0.01)
criterion = nn.MSELoss()

# Dummy data
inputs = torch.tensor([[1.0, 2.0]])
target = torch.tensor([[0.0, 1.0]])

# Training loop
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, target)
loss.backward()
optimizer.step()
            

6.1 Use GPU

Utilize CUDA for computations by moving your tensors and models to the GPU.

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
x = x.to(device)
            

6.2 Data Augmentation

Enhance your training dataset with augmentation techniques to improve model generalization.