Course · Intermediate
Neural Network Fundamentals
In this course, you will establish a solid foundation in deep learning concepts and techniques. You'll learn about the fundamental math and concepts that underpin deep learning models. This course is the first step in a series of courses that will take you on a journey from beginner to advanced deep learning practitioner.
- Intermediate friendly
- 1 hrs
- 3 lessons
- Free
Course overview
Establish a strong foundation in neural network architectures, core mathematics, and training methods that underpin modern deep learning and GPT models.
What's inside
3 lessons
- 01 6 minIntroduction to the Zero to GPT Course
Learn the basics of Generative Pre-trained Transformer (GPT) models.
- 02 20 minMath and NumPy fundamentals
<p><span style="color: rgb(36,41,47);background-color: rgb(255,255,255);font-size: 16px;">This is an optional lesson with a basic refresher on linear algebra and calculus for deep learning. We'll use NumPy to apply the concepts. If you're already familiar with these topics, you can skip this lesson.</span> </p>
- 03 8 minGradient Descent
<p>In this lesson, we'll learn about gradient descent by using it to train a linear regression model to predict tomorrow's temperature using historical data. Gradient descent is a technique that helps us set the correct values for neural network parameters. Without gradient descent, networks wouldn't be able to learn how to make predictions from data.</p>
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