NYU Deep Learning Spring 2020
This is the notes for the Spring 2019 Deep Learning course at NYU. This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional net and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
This is the initial draft of the course notes - they are based off of a course developed for the the African Masters of Machine Intelligence (AMMI). You can access that version here
The African Masters of Machine Intelligence (AMMI) is Africa's flagship program in machine intelligence led by The African Institute for Mathematical Sciences (AIMS). These lessons, developed during the course of several years while I've been teaching at Purdue and NYU, are here proposed for the AMMI (AIMS).
Prior to this course delivered for AMMI (AIMS), an earlier version of this was delivered and video-recorded for the Computational and Data Science for High Energy Physics (CoDaS-HEP) summer school at Princeton University. Please refer to this version release here.
Click CoDaS-HEP_2018 to jump to this release.
These lessons, developed during the course of several years while I've been teaching at Purdue and NYU, are here proposed for the Computational and Data Science for High Energy Physics (CoDaS-HEP) summer school at Princeton University. The whole course has been recorded and the playlist is made available here. Check the slides for drawings of better visual quality.