Introduction to 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.

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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.