Tutorial 9- Drop Out Layers in Multi Neural Network
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After going through this video,
After going through this video, you will know:
Large weights in a neural network are a sign of a more complex network that has overfit the training data.
Probabilistically dropping out nodes in the network is a simple and effective regularization method.
A large network with more training and the use of a weight constraint are suggested when using dropout.
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Large weights in a neural network are a sign of a more complex network that has overfit the training data.
Probabilistically dropping out nodes in the network is a simple and effective regularization method.
A large network with more training and the use of a weight constraint are suggested when using dropout.
Below are the various playlist created on ML,Data Science and Deep Learning. Please subscribe and support the channel. Happy Learning!
Deep Learning Playlist: Tutorial 1- Introduction to Neural Ne...
Data Science Projects playlist: Generative Adversarial Networks using...
NLP playlist: Natural Language Processing|Tokenization
Statistics Playlist: Population vs Sample in Statistics
Feature Engineering playlist: Feature Engineering in Python- What a...
Computer Vision playlist: OpenCV Installation | OpenCV tutorial
Data Science Interview Question playlist: Complete Life Cycle of a Data Science...
You can buy my book on Finance with Machine Learning and Deep Learning from the below url
amazon url: https://www.amazon.in/Hands-Python-Fi...
🙏🙏🙏🙏🙏🙏🙏🙏
YOU JUST NEED TO DO
3 THINGS to support my channel
LIKE
SHARE
&
SUBSCRIBE
TO MY YOUTUBE CHANNEL
5 سال پیش
در تاریخ 1398/05/02 منتشر شده
است.
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