User bibliolytic

10 votes

Why should we shuffle data while training a neural network?

answered Sep 6, 2017 at 6:34

5 votes

Why PCA needs to consider perpendicular distance and cater for maximal variance?

answered Nov 6, 2017 at 8:37

4 votes

Should we remove duplicates when training a SVM?

answered Oct 26, 2017 at 6:33

4 votes

Standardization for Regularized Regression

answered Oct 4, 2017 at 7:50

4 votes

Biased Data in Machine Learning

answered Sep 5, 2017 at 13:39

3 votes

Better performance using Random Forest one-Vs-All than Random Forest multiclass?

answered Sep 6, 2017 at 12:30

3 votes

Dropout effectiveness on small neural networks

answered Oct 16, 2017 at 9:02

2 votes

Finding Theft in Tender Sales

answered Nov 17, 2017 at 12:57

2 votes

PCA with anomaly detection

answered Sep 6, 2017 at 12:40

1 vote

Supervised Approach for Longitudinal Data

answered Sep 8, 2017 at 6:57

1 vote

Accepted

How to perform L2 Normalization for machine learning

answered Sep 8, 2017 at 7:05

1 vote

Accepted

Choosing neural network architeture and how to know if it is overfitting

answered Sep 6, 2017 at 6:27

1 vote

Implementing a CNN using tensorflow

answered Jan 12, 2017 at 10:20

1 vote

Comparing shapes of distribution

answered Sep 5, 2017 at 13:31

1 vote

Using PCA to perform feature selection when variability and correlation are the only selection critera?

answered Oct 6, 2017 at 13:23

1 vote

One-class SVM: "training set vs. origin" logic

answered Nov 17, 2017 at 12:43

1 vote

Training LeNet on MNIST with frozen layers

answered Nov 20, 2017 at 9:13

1 vote

Mapping one feature space to another for prediction purposes?

answered May 3, 2019 at 13:17

1 vote

Why does the R2 value of regression improve on retraining neural network

answered May 7, 2019 at 15:00

0 votes

Accepted

Adding feautre by polynomial expansion drastically decreases the training set accuracy

answered Nov 1, 2017 at 14:18

0 votes

Classification using linear regression

answered Nov 3, 2017 at 14:23

0 votes

Best way to train one-class SVM

answered Oct 4, 2017 at 12:56

0 votes

How can I approximate the accurate sensor readings given readings from noisy sensors?

answered Oct 5, 2017 at 14:00

0 votes

Time series and images : difference and terminology

answered Jan 16, 2017 at 16:33

Stack Exchange Network

24 Answers

Why should we shuffle data while training a neural network?

Why PCA needs to consider perpendicular distance and cater for maximal variance?

Should we remove duplicates when training a SVM?

Standardization for Regularized Regression

Biased Data in Machine Learning

Better performance using Random Forest one-Vs-All than Random Forest multiclass?

Dropout effectiveness on small neural networks

Finding Theft in Tender Sales

PCA with anomaly detection

Supervised Approach for Longitudinal Data

How to perform L2 Normalization for machine learning

Choosing neural network architeture and how to know if it is overfitting

Implementing a CNN using tensorflow

Comparing shapes of distribution

Using PCA to perform feature selection when variability and correlation are the only selection critera?

One-class SVM: "training set vs. origin" logic

Training LeNet on MNIST with frozen layers

Mapping one feature space to another for prediction purposes?

Why does the R2 value of regression improve on retraining neural network

Adding feautre by polynomial expansion drastically decreases the training set accuracy

Classification using linear regression

Best way to train one-class SVM

How can I approximate the accurate sensor readings given readings from noisy sensors?

Time series and images : difference and terminology