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CSCI-GA 3033-091
Introduction to Deep Learning Systems
Welcome to the course on Deep Learning Systems
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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.
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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.
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Emphasis will be on getting a working knowledge of tools and techniques for the performance evaluation of DL systems.
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You will gain practical experience working on different stages of the DL life cycle, including model development, testing, and deployment.
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The assignments will be mostly hands-on involving standard DL frameworks (Tensorflow, Pytorch) and open source technologies.
Grade Distribution
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Course Faculty
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Instructor
Parijat Dube
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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