Build The Self-Attention in PyTorch From Scratch

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Build The Self-Attention in PyTorch From Scratch

Hosted by Damien Benveniste

Fri, May 2, 2025

4:30 PM UTC (1 hour 30 minutes)

Virtual (Zoom)

Free to join

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235 students

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Build Production-Ready LLMs From Scratch

Damien Benveniste

View syllabus

Fri, May 2, 2025

4:30 PM UTC (1 hour 30 minutes)

Virtual (Zoom)

Free to join

235 students

Invite your network

Go deeper with a course

Build Production-Ready LLMs From Scratch

Damien Benveniste

View syllabus

What you'll learn

Master the core of the Transformer Architecture

You’ll translate the mathematical formula into PyTorch code, so you can debug the very heart of every LLM.

Build a Fully Functional Multi‑Head Self‑Attention Module

Parallelize multiple attention heads on Q/K/V tensors, concat head outputs, and apply final linear projection.

Validate Attention on Toy Inputs

Test your module with sample token embeddings, verify output shapes, and inspect attention score matrices.

Why this topic matters

Building self‑attention from scratch bridges theory and practice. You’ll master the core LLM mechanism, customizing, debugging, and optimizing attention layers, which hiring managers prize for production AI. After this lesson, you’ll own runnable PyTorch code and the confidence to tackle full Transformer blocks and advanced LLM workflows.

You'll learn from

Damien Benveniste

Former Meta ML Tech Lead, CEO @ AiEdge

Welcome, my name is Damien Benveniste! After a Ph.D. in theoretical Physics, I started my career in Machine Learning more than 10 years ago.

I have been a Data Scientist, Machine Learning Engineer, and Software Engineer. I have led various Machine Learning projects in diverse industry sectors such as AdTech, Market Research, Financial Advising, Cloud Management, online retail, marketing, credit score modeling, data storage, healthcare, and energy valuation. Previously, I was a Machine Learning Tech Lead at Meta on the automation at scale of model optimization for Ads ranking.

I am now training the next generation of Machine Learning engineers.

Previously at