Uploaded August 2018 | Updated September 2026, 2 weeks ago
Watch this series from the start at youtube.com/playlist?list=PLsu0TcgLDUiIKPMXu1k_rItoTV8xPe1cj
In this video I show you how to implement l2 regularization and dropout in Keras. The dataset used in this video lecture is the IMDB database that can be downloaded using Keras.
Densely connected networks results in models with high variance. In the video, three such models are created to demonstrate the overfitting problem. There are also two models, one with l2-regularization and one with dropout, to show you the effect that these have on the validation(test) data.
The video was recorded using RStudio and also includes some tips and tricks. There are a few plots created with Plotly. You can watch videos on Plotly for R at youtube.com/watch?v=OrKh-mbc7Sg&list=PLsu0TcgLDUiLnADl09N4nDQMcFn3AaF7G
Read the RPubs document at rpubs.com/juanhklopper/regularization_dropout
Download the R markdown file at github.com/juanklopper/Deep-learning-using-R
Watch this series from the start at youtube.com/playlist?list=PLsu0TcgLDUiIKPMXu1k_rItoTV8xPe1cj
In this video I show you how to implement l2 regularization and dropout in Keras. The dataset used in this video lecture is the IMDB database that can be downloaded using Keras.
Densely connected networks results in models with high variance. In the video, three such models are created to demonstrate the overfitting problem. There are also two models, one with l2-regularization and one with dropout, to show you the effect that these have on the validation(test) data.
The video was recorded using RStudio and also includes some tips and tricks. There are a few plots created with Plotly. You can watch videos on Plotly for R at youtube.com/watch?v=OrKh-mbc7Sg&list=PLsu0TcgLDUiLnADl09N4nDQMcFn3AaF7G
Read the RPubs document at rpubs.com/juanhklopper/regularization_dropout
Download the R markdown file at github.com/juanklopper/Deep-learning-using-R
