Introduction To Deep Learning

Catalog description

This course will provide both theoretical and practical insights into deep learning networks applied to science. Students will gain a deep understanding of cutting-edge deep learning architectures and their practical applications. Through this course, students will receive guidance on creating and implementing deep learning models from scratch. They will learn how to train deep learning models with data augmentation techniques and enhance their understanding of deep learning security. In addition to delving into various deep-learning applications, students will actively participate in hands-on course labs and projects. These projects will encompass a wide range of tasks, including object detection, facial expression recognition, handwriting analysis, and natural language processing. Course Learning Outcomes 1. Master how to design/implement deep learning from scratch, and how to train deep learning networks effectively. 2. Learn how to optimize deep learning models, tuning data augmentation, and to improve deep learning security. 3. Apply knowledge through hands-on course labs and projects, including object detection, facial expression recognition, handwriting analysis, and natural language processing. View Class Schedule