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Optimization I

Difficulty level

This lecture covers the concepts of gradient descent, stochastic gradient descent, and momentum. It is a 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 module include: Models 1-7 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

Topics covered in this lesson


0:01:28 – Gradient Descent
0:14:58 – Stochastic Gradient Descent
0:27:52 – Momentum
0:44:35 – Adaptive Methods
1:05:07 – Normalization Layers
1:20:17 – The Death of Optimization