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A typical loss metric in regression problems
Discussing the primal computation
Task: Backward propagate cotangent information
Relevant for backpropagation in Neural Networks (as part of Deep Learning)
Often not interested in reference solution cotangent
General vector-Jacobian product (pullback rule
Finding a closed-form expression for the Jacobian
Changing to index notation
Back to symbolic notation
The other Jacobian
Plugging Jacobians into vJp rule
Full Pullback rule
Some remarks
Outro
Intro: Welcome to "Is it Observable?"
What is Vector: Comprehensive Overview and Key Features
Design Experience: The Philosophy Behind Vector
Source: Exploring Vector’s Log and Metric Ingestion
Transform: Data Transformation with Vector
Vector Remap Language: Unlocking VRL for Advanced Data Manipulation
Sink: Exporting Data Efficiently with Vector’s Sinks
Observability: Advanced Monitoring and Troubleshooting with Vector
Conclusion: Key Takeaways and Final Thoughts