Build Your First Transformer Model in 46 Minutes – From Scratch to Insight

About Course

Transformers are the revolutionary architecture behind today’s most powerful AI systems — from GPT to BERT, DALL·E to T5. In just 46 minutes, this hands-on crash course gives you everything you need to understand, build, and experiment with your own Transformer model from scratch.

Whether you’re a Python enthusiast or looking to scale your AI/ML skills fast, this course is your shortcut to mastering one of the most important concepts in deep learning — without wasting hours in theory-heavy lectures.

What You’ll Learn:

What Transformer models are and why they matter
The attention mechanism and how it works
How to implement positional encoding
Building a Transformer block from scratch
Feeding real data through your model
Final project: Training your first mini-transformer

What’s Included:

Full access to GitHub code and clean Jupyter notebooks
Real-world dataset to test your model
46 minutes of focused, to-the-point content
Downloadable notes, references, and bonus resources
Lifetime access and certificate of completion

Prerequisites:

No prior ML experience required.
However, a decent understanding of Python (lists, loops, functions, NumPy) will help you follow along comfortably.

Who Is This For?

Python developers exploring AI
Data science students wanting to go deeper
ML beginners who want results fast
Anyone curious about how ChatGPT and modern AI really work under the hood

The core concepts behind Transformer architecture used in models like GPT, BERT, and T5
How self-attention and multi-head attention mechanisms work
The role of positional encoding in processing sequences
How to build a basic Transformer block from scratch using Python
Step-by-step coding of a mini-Transformer using NumPy and PyTorch
How to prepare and feed real-world text data into a Transformer model
How Transformers differ from RNNs, LSTMs, and CNNs — and why they’re better
How to visualize and interpret attention scores
Hands-on training of a working Transformer model
How to apply these skills to scale your own AI/ML projects

Course Content

Unlock the power behind GPT — Build your first Transformer model in just 1 hour!
Curious about how AI giants like GPT, BERT, and T5 actually work? In this fast-paced, beginner-friendly 1-hour demo, you’ll discover the core architecture powering today’s most advanced AI: the Transformer model. We'll break down complex ideas like self-attention, multi-head attention, and positional encoding—all in simple terms, with live coding examples using PyTorch or TensorFlow. By the end, you won’t just understand Transformers—you’ll build your own mini Transformer model from scratch. What You’ll Learn: What makes Transformers the backbone of modern NLP How self-attention and multi-head attention work The core components of a Transformer model Step-by-step live coding of a simple Transformer Real-world use cases: from text generation to translation This session is perfect for students, developers, or ML beginners who want a hands-on introduction to the model that changed AI forever. No deep math. No fluff. Just the fundamentals, simplified and built live. Join now and unlock the architecture behind GPT in just 1 hour.

Build Your First Transformer Model

46:00

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