Course

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.

Enroll for free

Part of the Zero to GPT path.

4.8 (359 reviews)

4,666 learners enrolled in this course.

Intermediate friendly
1 hours
Self paced
3 lessons

“The learning paths on Dataquest are incredible. They give you a direction through the learning process – you don’t have to guess what to learn next.”

Otávio Silveira

Data Analyst @ Hortifruti

Neural Network Fundamentals

Enroll For Free

Course overview

The course has several objectives. First, you will learn what a GPT model is (Generative Pre-trained Transformer) and how it operates. Additionally, the course will help you refresh your knowledge of linear algebra and calculus concepts that are crucial for deep learning. You’ll learn how to manipulate matrices, perform vector calculus, and solve optimization problems.

Finally, you will learn how gradient descent is used in deep learning. You’ll learn how to train a linear regression model using gradient descent, and you’ll learn how gradient descent is used to optimize neural network parameters. You’ll also gain practical experience by implementing gradient descent algorithms from scratch using Python.

By the end of this course, you’ll have a solid understanding of the fundamental concepts of deep learning. You’ll be well-prepared to continue your deep learning journey and move on to more advanced topics such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), leading up to training your own GPT model.

Key skills

Explain how a GPT (Generative Pre-trained Transformer) model operates
Refresh linear algebra and calculus knowledge for deep learning
Train a linear regression model using gradient descent

Course outline

Neural Network Fundamentals [3 lessons]

Introduction to the Zero to GPT Course 1h

Lesson Objectives

Define what a Generative Pre-trained Transformer (GPT) model is
Explain how a GPT model operates
Familiarize oneself with the Dataquest teaching method
Identify the course prerequisites

Math and NumPy fundamentals 1h

Lesson Objectives

Perform vector and matrix operations using NumPy
Minimize the model error with the normal equation
Perform element-wise operations on arrays of different shapes using broadcasting
Calculate the derivative of a function

Gradient Descent 1h

Lesson Objectives

Explain the fundamentals of gradient descent
Train a linear regression model
Calculate and minimize loss

The Dataquest guarantee

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

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Aaron Melton

Business Analyst at Aditi Consulting

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”

Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

Victoria E. Guzik

Associate Data Scientist at Callisto Media

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”