Skip to content

This task focuses on training the MINIST dataset to get a feel for the differences between fully connected networks, neural networks, and the effects of different activation functions, training techniques (BN, Dropout, and L2 regularization) on the network. 本次任务主要是通过对MINIST数据集进行训练来感受全连接网络、神经网络的差异,以及不同激活函数、训练技巧(BN,Dropout以及L2正则化)对网络的影响。

Notifications You must be signed in to change notification settings

zggg1p/MINIST-Dataset-Classification

Folders and files

NameName
Last commit message
Last commit date

Latest commit

Feb 10, 2022
1a65ee0 · Feb 10, 2022

History

3 Commits
Feb 10, 2022
Feb 10, 2022
Feb 10, 2022
Feb 10, 2022
Feb 10, 2022
Feb 10, 2022
Feb 10, 2022
Feb 10, 2022
Feb 10, 2022

Repository files navigation

MINIST-Dataset-Classification

This task focuses on training the MINIST dataset to get a feel for the differences between fully connected networks, neural networks, and the effects of different activation functions, training techniques (BN, Dropout, and L2 regularization) on the network.
本次任务主要是通过对MINIST数据集进行训练来感受全连接网络、神经网络的差异,以及不同激活函数、训练技巧(BN,Dropout以及L2正则化)对网络的影响。

About

This task focuses on training the MINIST dataset to get a feel for the differences between fully connected networks, neural networks, and the effects of different activation functions, training techniques (BN, Dropout, and L2 regularization) on the network. 本次任务主要是通过对MINIST数据集进行训练来感受全连接网络、神经网络的差异,以及不同激活函数、训练技巧(BN,Dropout以及L2正则化)对网络的影响。

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages