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Introduction to Programming with Python
Overview
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Learn to track, visualize, and optimize machine learning experiments using Weights & Biases through this comprehensive video course playlist that provides a gentle introduction to the W&B platform with a focus on experiment tracking. Master the fundamentals of instrumenting W&B in your code to monitor model performance, explore the W&B workspace interface for managing your projects, and develop skills in comparing and analyzing multiple experiments to identify the best performing models. Discover advanced features beyond basic experiment tracking including automated workflows for updating and deploying models on edge devices, and explore how W&B can streamline collaboration with your team while making your ML projects more efficient and reproducible. The course covers essential topics from initial setup and code instrumentation through workspace navigation, experiment comparison techniques, and advanced automation features that enable seamless model deployment workflows.
Syllabus
Introducing W&B
Instrumenting W&B in your code
Exploring W&B workspace
Comparing & analyzing experiments
Using W&B beyond experiment tracking
W&B Automations to update and deploy models on Edge Devices
Taught by
Weights & Biases