Explore the essential components of deep learning model development in this comprehensive 52-minute lecture. Gain insights into software engineering practices, deep learning frameworks, and meta-frameworks. Delve into distributed training techniques and understand the role of GPUs in accelerating computations. Learn about effective compute resource management and experiment management strategies. Discover the tools and infrastructure necessary to build and deploy sophisticated deep learning models, from foundational software engineering principles to advanced distributed training methodologies.
Overview
Syllabus
Development Infrastructure & Tooling
Software Engineering
Deep Learning Frameworks
Meta-frameworks and model zoos
Distributed Training
GPUs
Compute Resource Management
Experiment Management
Taught by
The Full Stack
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