This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: introduction to data science or a graduate-level machine learning course.
Courses in this collection
This module provides an introduction to the motivation of deep learning and its history and inspiration. The module covers the concepts of:…
This module covers the concepts of recurrent and convolutional nets (theory and practice), natural signals properties and the convolution, and…
This module is intended to provide a foundation in energy based models. It is a part of the Deep Learning Course at CDS, a course that covered the…