Deep Learning Systems | Carnegie Mellon University

Comprehensive introduction to deep learning systems, covering machine learning, neural networks, optimization, and hardware acceleration. Hands-on implementation of key concepts.

Introduction

This course provides a comprehensive introduction to deep learning systems, covering topics such as machine learning fundamentals, neural network architectures, optimization techniques, hardware acceleration, and more. Students will gain practical experience through hands-on implementation of key deep learning concepts.

Highlights

• In-depth coverage of deep learning fundamentals, from softmax regression to convolutional and recurrent neural networks
• Hands-on implementation of automatic differentiation, neural network libraries, and hardware acceleration techniques
• Exploration of advanced topics like transformers, generative models, and model deployment
• Lectures and assignments from renowned experts in the field of deep learning systems

Recommendation

This course is highly recommended for students and professionals interested in gaining a deep understanding of deep learning systems and their practical implementation. It is suitable for those with a background in machine learning, computer science, or a related field, and who are looking to deepen their knowledge and skills in the rapidly evolving field of deep learning.