CSCI-GA 3033-091

Introduction to Deep Learning Systems

Welcome to the course on Deep Learning Systems

  • The course covers algorithmic and system-related building blocks of Deep Learning systems, such as training algorithms, network architectures, and best practices for their performance optimization.

  • You will learn hyperparameter selection, scalable distributed DL training, Kubernetes-based DL system stack on cloud, tools, and benchmarks for performance evaluation of DL systems, transfer learning, and other related concepts. 

  • Emphasis will be on getting a working knowledge of tools and techniques for the performance evaluation of DL systems. 

  • You will gain practical experience working on different stages of the DL life cycle, including model development, testing, and deployment. 

  • The assignments will be mostly hands-on involving standard DL frameworks (Tensorflow, Pytorch) and open source technologies.
     

Grade Distribution

Course Faculty

Instructor

Parijat Dube

pd2216@nyu.edu

Office Hours:

Wednesday

5:00 PM - 6:00 PM EST

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Thursday

9:00 AM - 10:00 AM EST

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Course Assistant

Kshitij Sanghvi

kbs391@nyu.edu

Office Hours:

Thursday

9:00 PM - 10:00 PM EST

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Grader

Yuan Shen

Grader

Umang Sharma

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