Fundamentals of Deep Learning
Master how neural networks and deep learning power today’s AI systems, with hands-on projects and real-world applications from structured data to computer vision.
Course Information
Certificate Track
About this Course
Dive into the world of neural networks and deep learning to understand how modern AI systems learn from data. This course helps you build a clear understanding of how networks process both structured and unstructured data, giving you the intuition to see how models make predictions and avoid common pitfalls like overfitting and underfitting.
Fundamentals of Deep Learning is one of the five courses in Universal AI, a self-paced program designed to help you go from beginner to AI authority—no coding or technical skills required.
What you'll learn
- Understand the basics of neural networks, including perceptrons, multilayer networks, and embeddings
- Learn how models handle structured and unstructured data
- Build and train neural networks using modern deep learning frameworks
- Apply optimization and regularization techniques to improve performance and avoid overfitting
- Design and evaluate models for real-world tasks like classification and regression
- Explore convolutional neural networks and transfer learning for computer vision applications
Modules
This course has 3 modules
Meet your instructors
Léonard Boussioux
Assistant Professor, Foster School of Business, University of Washington; Adjunct Assistant Professor in Computer Science, Allen School of Computer Science and Engineering; Affiliate Faculty, Laboratory for Innovation Science at Harvard University
Léonard Boussioux's research and entrepreneurial work have received wide recognition, including giving two TEDx talks, winning the MIT 3-minute thesis competition, and awards from INFORMS, MIT, IEEE, UNESCO, and Google. He previously worked at Google X, Mila, UC Berkeley, and the French National Centre for Scientific Research. Léonard is also a passionate teacher and has received eight teaching awards at the University of Washington and MIT, including the Goodwin Medal.
