chris/fastbook
1
0
Fork 0
mirror of https://github.com/fastai/fastbook.git synced 2025-04-04 18:00:48 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix type "check" -> "checks" (#408)

Browse Source
This commit is contained in:
Jophel Lyles 2022-04-25 02:15:39 -04:00 committed by GitHub
parent fc8418aff3
commit 2e722af6bc
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 1 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
01_intro.ipynb
View File

@ -1563,7 +1563,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"**Overfitting is the single most important and challenging issue** when training for all machine learning practitioners, and all algorithms. As you will see, it is very easy to create a model that does a great job at making predictions on the exact data it has been trained on, but it is much harder to make accurate predictions on data the model has never seen before. And of course, this is the data that will actually matter in practice. For instance, if you create a handwritten digit classifier (as we will very soon!) and use it to recognize numbers written on checks, then you are never going to see any of the numbers that the model was trained on—check will have slightly different variations of writing to deal with. You will learn many methods to avoid overfitting in this book. However, you should only use those methods after you have confirmed that overfitting is actually occurring (i.e., you have actually observed the validation accuracy getting worse during training). We often see practitioners using over-fitting avoidance techniques even when they have enough data that they didn't need to do so, ending up with a model that may be less accurate than what they could have achieved."
"**Overfitting is the single most important and challenging issue** when training for all machine learning practitioners, and all algorithms. As you will see, it is very easy to create a model that does a great job at making predictions on the exact data it has been trained on, but it is much harder to make accurate predictions on data the model has never seen before. And of course, this is the data that will actually matter in practice. For instance, if you create a handwritten digit classifier (as we will very soon!) and use it to recognize numbers written on checks, then you are never going to see any of the numbers that the model was trained on—checks will have slightly different variations of writing to deal with. You will learn many methods to avoid overfitting in this book. However, you should only use those methods after you have confirmed that overfitting is actually occurring (i.e., you have actually observed the validation accuracy getting worse during training). We often see practitioners using over-fitting avoidance techniques even when they have enough data that they didn't need to do so, ending up with a model that may be less accurate than what they could have achieved."
]
},
{