Introduction of PyTorch

आपने TensorFlow और Keras सीख लिया — अब बारी है PyTorch की, जो research और flexibility के लिए Deep Learning community में सबसे लोकप्रिय framework है।

PyTorch एक open-source deep learning framework है जिसे Facebook AI Research Lab (FAIR) ने 2016 में विकसित किया।

यह framework खासकर researchers और advanced developers के बीच पसंदीदा है, क्योंकि यह:

• Dynamic computation graph प्रदान करता है (runtime पर बदलता है)
• Pythonic और NumPy जैसा syntax देता है
• GPU acceleration आसानी से करता है

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🔑 Features:

Feature	Description
🧮 Dynamic Graphs	Real-time control (ज़्यादा flexibility)
📊 Tensor Library	NumPy जैसे operations with GPU support
🧠 Autograd	Automatic gradient calculation
🔧 Modular API	Neural nets = Modules
🖥️ GPU Ready	CUDA support

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🔶 2. PyTorch Installation

pip install torch torchvision

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🔷 3. Tensors in PyTorch

Tensors are multi-dimensional arrays (similar to NumPy arrays) but with GPU support.

import torch

x = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
print(x.shape)           # torch.Size([2, 2])
print(x + x)             # Tensor addition
print(x @ x.T)           # Matrix multiplication

✅ Use GPU:

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

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🔷 4. Autograd – Automatic Differentiation

x = torch.tensor(2.0, requires_grad=True)
y = x**3 + 2*x
y.backward()
print(x.grad)   # dy/dx = 3x^2 + 2 = 3*2^2 + 2 = 14

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🔷 5. Building a Simple Neural Network

🔨 Step 1: Import Libraries

🔨 Step 1: Import Libraries

import torch
import torch.nn as nn
import torch.optim as optim

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🔨 Input and Output for XOR

X = torch.tensor([[0.,0.],[0.,1.],[1.,0.],[1.,1.]])
y = torch.tensor([[0.],[1.],[1.],[0.]])

🔨 Step 2: Define Model

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class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__()
        self.fc1 = nn.Linear(2, 4)
        self.fc2 = nn.Linear(4, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return torch.sigmoid(self.fc2(x))

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🔨 Step 3: Instantiate Model

model = MyNet()

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🔨 Step 4: Define Loss and Optimizer

criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)

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🔨 Step 5: Train the Model

for epoch in range(100):
    y_pred = model(X)
    loss = criterion(y_pred, y)
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    print(f"Epoch {epoch}, Loss: {loss.item()}")

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🔧 Important PyTorch Modules

Module	Description
torch.Tensor	Main data structure
torch.nn	For building neural nets
torch.nn.functional	Activation functions, loss functions
torch.optim	Optimizers like Adam, SGD
torch.utils.data	Dataset and DataLoader tools
torchvision	Image datasets and transformations

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🔷 6. Example: XOR with MLP

X = torch.tensor([[0.,0.],[0.,1.],[1.,0.],[1.,1.]])
y = torch.tensor([[0.],[1.],[1.],[0.]])

model = nn.Sequential(
    nn.Linear(2, 4),
    nn.ReLU(),
    nn.Linear(4, 1),
    nn.Sigmoid()
)

loss_fn = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.1)

for epoch in range(2000):
    y_pred = model(X)
    loss = loss_fn(y_pred, y)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if epoch % 200 == 0:
        print(f"Epoch {epoch}, Loss: {loss.item()}")

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📝 Practice Questions:

1. PyTorch और TensorFlow में मुख्य अंतर क्या है?
2. PyTorch में tensor कैसे बनाते हैं?
3. Autograd का उपयोग gradient के लिए कैसे करते हैं?
4. एक simple model class कैसे बनाते हैं?
5. Sequential API और custom model class में क्या फर्क है?

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🧠 Summary Table

Concept	Explanation
Tensor	PyTorch का data container (NumPy + GPU)
Autograd	Automatic differentiation
nn.Module	Neural network architecture का base class
Optimizer	Parameters को update करता है
Loss Function	Model की गलती measure करता है