LLM Mastery in 30 Days: Day 4 - Transformer from Scratch (PyTorch)

🔍 In this video, we'll dive deep into the Transformer architecture and break down the math behind it step-by-step, all while keeping the implementation concise! 🚀 By the end of this video, you'll know how to write a Transformer in under 150 lines of code with an easy-to-follow, line-by-line explanation.

📌 What You'll Learn:
What is the Transformer Architecture?
A high-level overview of how the encoder and decoder work together.
Breaking Down Key Math Concepts
Understand self-attention, multi-head attention, and positional encoding through math and code.
Efficient Implementation
Learn to implement each component in Python with minimal code.
Line-by-Line Explanation
Detailed walkthrough of each function and logic to make sure you grasp every concept.
🛠️ Code Overview:
Attention Mechanisms: How queries, keys, and values interact.
Positional Encodings: Why we need them and how they're calculated.
Feedforward Networks: Adding non-linearity to our architecture.
Layer Normalization: Ensuring stable training.

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[UrvilPanchal]

Thanks a lot Vasanth, I am learning about the Transformer Architecture from You and Campus X channel, and now you've uploaded the video of its implementation. I can code along while learning its theory. Thanks a lot.

[Frost-Head]

Great work man we need people like you in deep learning space.

[agamergen]

Vasanth is the one who can explain tough stuff like to 8-year-old kid on this planet