From a98bedefec9017646ea16e3896323688ce826326 Mon Sep 17 00:00:00 2001
From: M MOHITH <mohith.cvv230152@cvv.ac.in>
Date: Wed, 11 Jun 2025 10:01:27 +0530
Subject: [PATCH] Add PyTorch cheatsheet under languages/

---
 languages/python-pytorch.md | 118 ++++++++++++++++++++++++++++++++++++
 1 file changed, 118 insertions(+)
 create mode 100644 languages/python-pytorch.md

diff --git a/languages/python-pytorch.md b/languages/python-pytorch.md
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+# PyTorch Cheatsheet
+
+A quick reference for using PyTorch (https://pytorch.org), a popular deep learning framework.
+
+## 📦 Importing PyTorch
+
+```python
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+```
+
+# Creating tensors
+
+```python
+x = torch.tensor([1.0, 2.0])
+x = torch.zeros(2, 3)
+x = torch.ones(2, 3)
+x = torch.rand(2, 3)
+```
+
+# Tensor properties
+
+```python
+x.shape
+x.dtype
+x.device
+```
+
+# Tensor operations
+
+```python
+x + y
+x @ y  # Matrix multiplication
+x.sum()
+x.view(6)  # Reshape
+```
+
+# Autograd (Automatic Differentiation)
+
+```python
+x = torch.tensor(2.0, requires_grad=True)
+y = x ** 2
+y.backward()
+x.grad  # dy/dx = 2x
+```
+
+# Neural Network Modules
+
+```python
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fc1 = nn.Linear(10, 50)
+        self.fc2 = nn.Linear(50, 1)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        return self.fc2(x)
+
+model = Net()
+```
+
+# Loss Functions
+
+```python
+loss_fn = nn.MSELoss()
+loss = loss_fn(output, target)
+```
+
+# Optimizers
+
+```python
+optimizer = optim.SGD(model.parameters(), lr=0.01)
+optimizer.zero_grad()
+loss.backward()
+optimizer.step()
+```
+
+# Dataset and DataLoader
+
+```python
+from torch.utils.data import Dataset, DataLoader
+
+# Custom dataset
+class MyDataset(Dataset):
+    def __init__(self):
+        self.data = ...
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+dataset = MyDataset()
+loader = DataLoader(dataset, batch_size=32, shuffle=True)
+```
+
+# Model Evaluation
+
+```python
+model.eval()
+with torch.no_grad():
+    output = model(input)
+```
+
+# Save and Load Model
+
+```python
+# Save
+torch.save(model.state_dict(), 'model.pth')
+
+# Load
+model.load_state_dict(torch.load('model.pth'))
+model.eval()
+```