👩🏫 Implementation Guidelines
Implementation guidelines and error anlysis
📚 Common Terms
I did my best, my project is still doing bad, what shall I do? 😥
Well, in this stage we have a criteria, is your model doing worse than humans (Because humans are quite good at a lot of tasks 👩🎓)? If yes, you can:
👩🏫 Get labeled data from humans
👀 Gain insight from manual error analysis; (Why did a person get this right? 🙄)
🔎 Better analysis of bias / variance 🔍
🤔 Note: knowing how well humans can do on a task can help us to understand better how much we should try to reduce bias and variance
🧐 Is your model doing better than humans?
Processes are less clear 😥
Suitable techniques will be added here
🤓 Study case
Let's assume that we have these two situations:
Even though training and dev errors are same we will apply different tactics for better performance
In Case1, We have
High Bias
so we have to focus on bias reduction techniques 🤔, in other words we have to reduce the difference between training and human errors the avoidable errorBetter algorithm, better NN structure, ......
In Case2, We have
High Variance
so we have to focus on variance reduction techniques 🙄, in other words we have to reduce the difference between training and dev errorsAdding regularization, getting more data, ......
We call this procedure of analysis Error analysis 🕵️
👀 Error Types Visualization
In computer vision issues,
human-level-error ≈ bayes-error
because humans are good in vision tasks
🤗 Problems that ML surpasses human level performance
Online advertising
Product recommendations
Logistics
Loan approvals
.....
✨ My Detailed Notes on Bias / Variance and Related Procedures
🤸♀️ It is recommended to
When we have a new project it is recommended to produce an initial model and then iterate over it until you get the best model, this is more practical than spending time building model theoretical and thinking about the best hyperparameter -which is almost impossible 🙄-
So, just don't overthink! (In both ML problems and life problems 🤗🙆)
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