Checklist for debugging neural networks
Tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.
debugging checklist systems-design tutorial article

• Start simple • Confirm your loss • Check intermediate outputs and connections • Diagnose parameters • Tracking your work

Don't forget to tag @ceceshao1 in your comment, otherwise they may not be notified.

Authors
Share this project
Similar projects
A Recipe for Training Neural Networks
The most common neural net mistakes and listing a few common gotchas related to training neural nets.
🚧 Simple considerations for simple people building fancy NNs
I will try to highlight a few steps of my mental process when it comes to building and debugging neural networks.
Visualizing Memorization in RNNs
Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use short-term or long-term contextual understanding.
TensorWatch
TensorWatch is a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in ...
Top collections