#Python | Cross Validation in Deep Learning | #CrossValidation #DeepLearning #Tensorflow смотреть онлайн

RJ Studio’s 91st video is conducting 5-fold Cross Validation to evaluate a simple Deep Learning model ✨
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The Boston Housing Price dataset used in this video only has around 400 samples. It is relatively a small dataset.
Therefore, if I create a training set AND a validation set from such a small dataset, the validation set is of course going to be very small in size too.

In consequence, the validation scores end up varying a lot, ie, having a high variance. And this will prevent us from evaluating the model accurately. ?

One solution to above problem is to conduct k-fold Cross Validation! ?


☆ Video Chapters ☆
0:00 Intro
0:43 Boston House Data
1:23 Feature Normalization
3:31 Network & Compilation
4:43 Cross Validation Fit
6:27 Analyze MAEs
7:59 Outro


A lot of explanation in this video is from a book titled,
"Deep Learning with Python" (Chapter 3)

Please check my GitHub for code: https://github.com/rjlee13/RJStudio_GH

Python logo in the thumbnail is from https://www.python.org/community/logos/




I am playing the background music. ^‿^
Check out my Music ? YouTube channel:
https://www.youtube.com/c/MusiCALUM/videos

Hope my video taught you something new!
And feel free to correct me if I made some mistakes in the comments :)


이번 비디오는 데이터의 샘플 수가 적을 경우 교차검증을 통해 딥러닝 신경망 모델을 평가하는 영상입니다. ≧◡≦
유익한 정보이길 바랄게요~ 감사합니다.


“In teaching others, we teach ourselves.”

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LinkedIn: https://www.linkedin.com/in/rj-lee/