PyTorch: Modern deep learning Frameworkand applications 2025 U

PyTorch, Facebook’s AI research lab developed by the UN open-source machine learning library. PyTorch is based on the Python programming language, especially deep learning has become the preferred framework for applications. Dynamic calculation charts and easy-to-use features, has gained popularity among researchers and developers.

Key features and benefits:

• Dynamic calculation charts
• Python integration
• Comprehensive GPU support
• Rich ecosystem and community support
• Ease of Debugging
• Pythonic structure

PyTorchta Tensors:
Tensors, PyTorchare the basic building blocks of. Multi-dimensional arrays that can be considered as tensors allows you to perform mathematical operations quickly and efficiently.

import torch

# Tensör oluşturma
x = torch.tensor([[1, 2, 3],
                 [4, 5, 6]])

# Temel işlemler
print(x.shape)  # Boyut bilgisi
print(x.dtype)  # Veri tipi

Simple Example Of A Neural Network:

import torch
import torch.nn as nn

class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.layer1 = nn.Linear(10, 5)
        self.layer2 = nn.Linear(5, 1)
        self.relu = nn.ReLU()
        
    def forward(self, x):
        x = self.relu(self.layer1(x))
        x = self.layer2(x)
        return x

# Model oluşturma
model = SimpleNN()

Data loading and Processing:
PyTorchDataLoader class flour batch data setss in the case facilitates the processing:

from torch.utils.data import DataLoader, Dataset

# Örnek veri seti
train_loader = DataLoader(
    dataset,
    batch_size=32,
    shuffle=True
)

Training Cycle:

criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())

for epoch in range(num_epochs):
    for batch in train_loader:
        optimizer.zero_grad()
        outputs = model(batch)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

PyTorchuses flour:

• Computer vision applications
• Natural language processing
• Voice recognition systems
• Time series analysis
• Reinforcement learning

PyTorch, a powerful framework that can be used in both research and production environmentsof the kind. Easy learning curve and thanks to the support of the wider community, machine learning has become a preferred choice for projects. Dynamic calculation, especially the graphics, thanks to facilitate the process and debug Pythonfame appropriate to the natural flow of the structure, for developers is a great advantage.