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Modules and architecture

Difficulty level

This lecture on modules and architectures is part of the Deep Learning Course at CDS, a course that covered 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. Prerequisites for this course include: Introduction to Data Science or a Graduate Level Machine Learning.

Topics covered in this lesson


00:00:00 – Welcome to class
00:00:38 – Non-linear functions
00:14:34 – Q&A
00:28:09 – Softargmax and softargmin
00:38:10 – Logsoftargmax
00:47:14 – Cost functions
00:58:39 – Architectures: multiplicative interaction
01:09:48 – Mixture of experts
01:27:50 – Parameter transformations