Path overview
This course stars with the fundamentals — neural network architectures and training methods. Later in the course, we’ll explore complex topics like transformers, GPU programming, and distributed training.
You’ll need to understand Python to take this course, including for loops, functions, and classes. The first part of this Dataquest path will teach you what you need.
To get the most out of this course, go through each chapter sequentially. Read the lessons or watch the optional videos — they have the same information. Look through the implementations to solidify your understanding, and recreate them on your own.
Key skills
- Learn how to train your own GPT model.
- Explore complex topics like transformers, GPU programming, and distributed training.
- Practice using gradient descent, dense networks, and classification with neural networks.
- Employ optimizers, PyTorch, regularization, and transformers.
Path outline
Part 1: Zero to GPT [3 courses]
Neural Network Fundamentals 1h
Objectives- Define what a GPT model is (Generative Pre-trained Transformer)
- Explain how a GPT model operates
- Refresh linear algebra and calculus knowledge for deep learning
- Understand how gradient descent is used in deep learning
Network Architectures 10h
Objectives- Understand different neural network architectures
- Build a dense neural network from scratch using Python
- Train a neural network on a classification task
- Predict sequence data using recurrent neural networks
Optimizing Network Parameters 10h
Objectives- Build a 2-layer deep learning framework from scratch
- Optimize network parameters
- Prevent overfitting through regularization
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