Zero to GPT: Train
Your Own GPT Model

Our new path “Zero to GPT” will take you from zero deep learning experience to training your own GPT model. You’ll learn everything from the basics of neural networks to cutting-edge techniques for optimizing transformer models. Don’t miss this early opportunity to upskill with GPT!

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You will need a few skills to complete the projects in the Zero to GPT path. You can brush up by completing these prerequisites.

Level 1

Introduction to Python, Data Visualization & Data Cleaning

This path covers basic Python, including for loops, functions, and classes.

Level 2

Machine Learning

This path teaches you the basics of machine learning, including how to train models, evaluate accuracy, and select features.

Zero to GPT Path

As AI moves out of the research lab, the world needs more people who can understand and apply it. If you want to be one of them, this course is for you.

Once you’ve mastered the prerequisites for learning how to build a GPT model from scratch, you can access the “Zero to GPT” course right here.


Intro to Zero to GPT

Discover how this path will take you from zero knowledge of deep learning to training your own GPT model.

Intro to NumPy

A basic (optional) refresher on linear algebra and calculus for deep learning using NumPy.

Gradient Descent From Scratch In Python

Understand the power of gradient descent, a fundamental optimization algorithm for training neural networks — and how to implement it from scratch in Python.

Neural Network From Scratch In Python

Master the theory of neural networks and leverage NumPy and Python to build and train a multi-layer neural network to predict the weather.

Classification With Neural Networks

Explore the theory behind classification, as well as how to implement it with neural networks, including the negative log likelihood loss function and the softmax activation.

RNN From Scratch In Python

Learn the theory behind recurrent neural networks (RNNs), then build one to predict the weather.

Optimizing Network Parameters

Gain understanding of neural network architecture, then build a miniature version of PyTorch to better understand backpropagation.

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