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Phase 3: Deep Learning (CNNs, RNNs, Transformers, Backprop)🔵 Intermediate40 min read1 Code Cells

Deep Learning Foundations: Multi-Head Self-Attention & Transformer Backpropagation

Derive Scaled Dot-Product Attention, Multi-Head projections, and backpropagation gradient flows in Transformer architectures.

Technical Article

Learning Objective: Derive Scaled Dot-Product Attention mathematically and implement Multi-Head Self-Attention (MHSA) in PyTorch from scratch.

Intuition & Big Picture

Self-Attention projects input embeddings into Query (Q), Key (K), and Value (V) space representations, allowing every token to attend to every other token in O(1) sequential steps.

Anil's Insight: Self-Attention is like a database query! Q is your SQL WHERE clause, K is the index, and V is the row contents returned!

Hands-On Code Implementation

Interactive Code Space

1 code cell available

In [1]:Python

400 font-semibold">import torch
400 font-semibold">import torch.nn 400 font-semibold">as nn
400 font-semibold">import torch.nn.functional 400 font-semibold">as F

400 font-semibold">class 400">MultiHeadSelfAttention(nn.Module):
    400 font-semibold">def 400">__init__(self, embed_dim, num_heads):
        400">super().400">__init__()
        self.c_attn = nn.400">Linear(embed_dim, 3 * embed_dim)
        self.c_proj = nn.400">Linear(embed_dim, embed_dim)

x = torch.400">randn(2, 8, 64)
400 font-semibold">print('Module Initialized 400 font-semibold">for Tensor Batch:', x.shape)

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Course Assets

1 Files

13th Nov 2025 - FineTuning.pdfPDF / Doc

🔒 All notes are rendered directly on page for fast reading.

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400 font-semibold">import torch 400 font-semibold">import torch.nn 400 font-semibold">as nn 400 font-semibold">import torch.nn.functional 400 font-semibold">as F 400 font-semibold">class 400">MultiHeadSelfAttention(nn.Module): 400 font-semibold">def 400">__init__(self, embed_dim, num_heads): 400">super().400">__init__() self.c_attn = nn.400">Linear(embed_dim, 3 * embed_dim) self.c_proj = nn.400">Linear(embed_dim, embed_dim) x = torch.400">randn(2, 8, 64) 400 font-semibold">print('Module Initialized 400 font-semibold">for Tensor Batch:', x.shape)

Deep Learning Foundations: Multi-Head Self-Attention & Transformer Backpropagation

Technical Article

Hands-On Code Implementation

Interactive Code Space

Key Assessment for Deep Learning Foundations: Multi-Head Self-Attention & Transformer Backpropagation: What is the primary operational advantage of this architecture?

Course Assets

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Deep Learning Foundations: Multi-Head Self-Attention & Transformer Backpropagation

Technical Article

Hands-On Code Implementation

Interactive Code Space

Key Assessment for Deep Learning Foundations: Multi-Head Self-Attention & Transformer Backpropagation: What is the primary operational advantage of this architecture?

Course Assets

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