/explore
|
You are here 
|
easystats.github.io | ||
| | | | |
datascienceworkshops.com
Blog article by Jeroen Janssens. Dec 13, 2019. 6 min read. Because ggplot2 is the de-facto package for creating high-quality data visualizations in R, and has been for a long time, there exists many excellent resources for learning ggplot2, including: the ggplot2 website, a two-page cheat sheet (PDF), Stack Overflow, and books such as ggplot2: Elegant Graphics for Data Analysis and R Graphics Cookbook: Practical Recipes for Visualizing Data. A ggplot2 cheat sheet (PDF) Two days ago, I published... |
|
| | | | | ||
| | | | |
matbesancon.xyz
Learning by doing: detecting fraud on bank notes using Python in 3 steps. |
|
| | | | | ||
| | | | |
finnstats.com
How to Plot Categorical Data in R-Quick Guide Categorical variables are data types that can be separated into categories. |
|
| | | | | ||
| | | | |
blog.keras.io
[AI summary] The text discusses various types of autoencoders and their applications. It starts with basic autoencoders, then moves to sparse autoencoders, deep autoencoders, and sequence-to-sequence autoencoders. The text also covers variational autoencoders (VAEs), explaining their structure and training process. It includes code examples for each type of autoencoder and mentions the use of tools like TensorBoard for visualization. The VAE section highlights how to generate new data samples and visualize the latent space. The text concludes with references and a note about the potential for further topics. |
|
| | | |||
