# 📚 PyTorch Tutorial for Beginners - Part 1/6: Fundamentals & Tensors
#PyTorch #DeepLearning #MachineLearning #NeuralNetworks #Tensors

Welcome to Part 1 of our comprehensive PyTorch series! This beginner-friendly lesson covers core concepts, tensor operations, and your first neural network.

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## 🔹 What is PyTorch?
PyTorch is an open-source deep learning framework developed by Facebook's AI Research Lab (FAIR). Key features:

✔️ Dynamic computation graphs (define-by-run)
✔️ GPU acceleration with CUDA
✔️ Pythonic syntax for intuitive coding
✔️ Automatic differentiation (autograd)
✔️ Rich ecosystem (TorchVision, TorchText, etc.)

import torch
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")

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## 🔹 Tensors: The Building Blocks
Tensors are PyTorch's multi-dimensional arrays (like NumPy but with GPU support).

### 1. Creating Tensors

# From Python list
a = torch.tensor([1, 2, 3])  # 1D tensor (vector)

# 2D tensor (matrix)
b = torch.tensor([[1., 2.], [3., 4.]])

# Special tensors
zeros = torch.zeros(2, 3)      # 2x3 matrix of zeros
ones = torch.ones_like(zeros)  # Same shape as zeros, filled with 1s
rand = torch.rand(3, 3)        # 3x3 matrix with uniform random values (0-1)

### 2. Tensor Attributes

x = torch.rand(2, 3)
print(f"Shape: {x.shape}")      # torch.Size([2, 3])
print(f"Data type: {x.dtype}")  # torch.float32
print(f"Device: {x.device}")    # cpu/cuda:0

### 3. Moving Tensors to GPU

if torch.cuda.is_available():
    x = x.to('cuda')  # Move to GPU
    print(f"Now on: {x.device}")  # cuda:0

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## 🔹 Tensor Operations
### 1. Basic Math

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

# Element-wise operations
add = x + y        # or torch.add(x, y)
sub = x - y
mul = x * y
div = x / y

# Matrix multiplication
mat1 = torch.rand(2, 3)
mat2 = torch.rand(3, 2)
matmul = torch.mm(mat1, mat2)  # or mat1 @ mat2

### 2. Reshaping Tensors

x = torch.arange(6)          # [0, 1, 2, 3, 4, 5]
x_reshaped = x.view(2, 3)    # [[0, 1, 2], [3, 4, 5]]
x_flattened = x.flatten()    # Back to 1D

### 3. Indexing & Slicing

x = torch.tensor([[1, 2], [3, 4], [5, 6]])
print(x[0, 1])    # 2 (first row, second column)
print(x[:, 0])    # [1, 3, 5] (all rows, first column)

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## 🔹 Autograd: Automatic Differentiation
PyTorch automatically computes gradients for tensors with requires_grad=True.

### 1. Basic Example

x = torch.tensor(2.0, requires_grad=True)
y = x**2 + 3*x + 1
y.backward()      # Compute gradients
print(x.grad)     # dy/dx = 2x + 3 → 7.0

### 2. Neural Network Context

# Simple linear regression
w = torch.randn(1, requires_grad=True)
b = torch.zeros(1, requires_grad=True)

# Forward pass
inputs = torch.tensor([[1.0], [2.0], [3.0]])
targets = torch.tensor([[2.0], [4.0], [6.0]])
predictions = inputs * w + b

# Loss and backward pass
loss = torch.mean((predictions - targets)**2)
loss.backward()  # Computes dloss/dw, dloss/db

print(f"Gradient of w: {w.grad}")
print(f"Gradient of b: {b.grad}")

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## **🔹 Your First Neural Network**
Let's build a single-layer perceptron for binary classification.

### 1. Define the Model

import torch.nn as nn

class Perceptron(nn.Module):
    def __init__(self, input_dim):
        super().__init__()
        self.linear = nn.Linear(input_dim, 1)  # 1 output neuron
        
    def forward(self, x):
        return torch.sigmoid(self.linear(x))  # Sigmoid for probability

model = Perceptron(input_dim=2)
print(model)

### 2. Synthetic Dataset

# XOR-like dataset
X = torch.tensor([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=torch.float32)
y = torch.tensor([[0], [1], [1], [0]], dtype=torch.float32)