🔥 PyTorch & Deep Learning Masterclass

Tensors & CUDA Acceleration, Dynamic Computation Graphs (Autograd), Custom Neural Networks (torch.nn), Loss Functions, Optimizers, CNNs aur Transformer Attention Blocks ka complete guide.

Curriculum Index

1. Module 1: Deep Learning Foundations & PyTorch vs TensorFlow
2. Module 2: PyTorch Tensors, Shapes & GPU CUDA Acceleration
3. Module 3: Automatic Differentiation with Autograd Engine
4. Module 4: Building Custom Architectures with torch.nn.Module
5. Module 5: The Standard 5-Step Training Loop & Optimizers
6. Module 6: Convolutional Neural Networks (CNNs) for Vision
7. Module 7: Modern Attention Mechanisms & Transformers

Module 01

1️⃣ Deep Learning Foundations & PyTorch vs TensorFlow

Definition: Deep Learning Machine Learning ka ek advanced subset hai jo multiple layers wale Artificial Neural Networks (ANNs) ka use karke raw, unstructured data (images, audio, text) se automatically hierarchical features learn karta hai.

Criteria	PyTorch (Meta AI)	TensorFlow / Keras (Google)
Computation Graph	Dynamic Graph (Define-by-Run): Debugging standard Python pdb se hoti hai.	Static Graph (Define-and-Run) via tf.function graph tracing.
Industry & Research	85%+ of recent academic papers, Hugging Face aur Generative AI models me primary choice.	Legacy enterprise deployments aur mobile embedded systems (TF Lite).
Pythonic Feel	100% native Python OOPs feel, NumPy jaise direct API calls.	Thoda declarative framework overhead lagta hai.

Module 02

2️⃣ PyTorch Tensors & GPU Acceleration

Tensor: Ek n-dimensional mathematical array jo GPU CUDA cores par massively parallel matrix multiplication support karta hai:

import torch

# 1. Device Agnostic Code (Automatically picks Nvidia GPU or Apple MPS or CPU)
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
print(f"Using compute device: {device}")

# 2. Tensor Creation & Reshaping
x = torch.randn(size=(4, 3, 224, 224), device=device) # Batch: 4, RGB: 3, 224x224 image
print(f"Tensor Shape: {x.shape} | Data Type: {x.dtype}")

# 3. Moving between Host RAM and GPU VRAM
cpu_tensor = x.to("cpu")

Module 03

3️⃣ Automatic Differentiation with Autograd

PyTorch ka autograd engine reverse-mode automatic differentiation implement karta hai. Calculus ke chain rule ko background directed acyclic graph (DAG) me automatically compute karta hai:

# Track gradients with requires_grad=True
w = torch.tensor(2.0, requires_grad=True)
b = torch.tensor(1.0, requires_grad=True)
x = torch.tensor(3.0)

# Forward Pass: y = w*x + b = 2*3 + 1 = 7
y = w * x + b

# Backward Pass (Backpropagation)
y.backward()

# Gradients d(y)/d(w) = x = 3.0, d(y)/d(b) = 1.0
print(f"Weight Gradient: {w.grad}") # Output: 3.0
print(f"Bias Gradient: {b.grad}")   # Output: 1.0

Module 04

4️⃣ Building Custom Architectures with torch.nn.Module

PyTorch me har deep neural network nn.Module base class ko subclass karke define hota hai:

import torch.nn as nn

class MultilayerPerceptron(nn.Module):
    def __init__(self, input_dim=784, hidden_dim=128, output_classes=10):
        super(MultilayerPerceptron, self).__init__()
        self.network = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.BatchNorm1d(hidden_dim),
            nn.ReLU(),
            nn.Dropout(p=0.2), # Regularization to prevent overfitting
            nn.Linear(hidden_dim, output_classes) # Raw Logits
        )

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

Module 05

5️⃣ The Standard 5-Step Training Loop

Deep learning models ko train karne ka universal 5-step PyTorch lifecycle:

The Universal 5 Steps:
1. optimizer.zero_grad() → Purane iteration ke accumulated gradients flush karna.
2. outputs = model(inputs) → Forward pass calculate karna.
3. loss = criterion(outputs, targets) → Loss / Error measure karna.
4. loss.backward() → Gradients calculate karna via backpropagation.
5. optimizer.step() → Weights update karna using gradient descent step ($w = w - \alpha \nabla w$).

Live PyTorch Neural Network Training Loop Simulator

[PyTorch Model Runtime]: Initialized MLP [784 → 128 → 10]. Device: CUDA. Optimizer: Adam(lr=0.001) | Criterion: CrossEntropyLoss() | Ready to train.

Module 06

6️⃣ Convolutional Neural Networks (CNNs)

Computer Vision me images ke spatial features (edges, textures, shapes) extract karne ke layers:

Layer	PyTorch Class	Primary Responsibility
Convolution	nn.Conv2d(in_channels, out_channels, kernel_size=3)	Slideable filter matrix images se local spatial patterns extract karta hai.
Activation	nn.ReLU()	Non-linearity introduce karta hai ($f(x) = \max(0, x)$).
Pooling	nn.MaxPool2d(kernel_size=2, stride=2)	Spatial dimensions (width × height) ko half karke compute speed badhata hai.

Module 07

7️⃣ Self-Attention & Modern Transformers

LLMs (GPT, LLaMA, BERT) ke foundation architecture ka core mathematical formula:

Scaled Dot-Product Attention:
$$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{Q K^T}{\sqrt{d_k}}\right) V$$
• Query ($Q$): "Main kya dhoondh raha hoon?"
• Key ($K$): "Mera content kisse match karta hai?"
• Value ($V$): "Mera actual semantic meaning kya hai?"
• $\sqrt{d_k}$ Scaling: Large dimensions par gradients ko vanish hone se rokta hai.

# PyTorch Built-in Multihead Attention Block
attention_layer = nn.MultiheadAttention(embed_dim=512, num_heads=8, batch_first=True)
attn_output, attn_weights = attention_layer(query=x, key=x, value=x)