Deep Learning | Carnegie Mellon University

Explore the fundamentals of deep learning, from basic concepts to advanced topics, with hands-on experience in PyTorch. Prepare to understand and extend the current literature on deep learning.

Introduction

The course presents the subject through a series of seminars and labs, which will explore it from its early beginnings, and work themselves to some of the state of the art. The seminars will cover the basics of deep learning and the underlying theory, as well as the breadth of application areas to which it has been applied, as well as the latest issues on learning from very large amounts of data.

Highlights

• Covers the fundamentals of deep learning, from basic concepts to advanced topics such as attention and sequence-to-sequence models
• Provides hands-on experience with PyTorch, a popular deep learning framework
• Offers a well-rounded curriculum with a mix of lectures, labs, and projects
• Prepares students to understand and extend the current literature on deep learning

Recommendation

This course is recommended for students who are interested in gaining a strong foundation in deep learning and its applications. It is suitable for both beginners and those with prior experience in machine learning, as it covers a wide range of topics and provides practical skills for implementing deep learning models.