Machine learning interpretability and explainable AI are hottest topics in data world nowadays. Even though linear regression is ignored by … More
Author: Sefik Serengil
Race and Ethnicity Prediction in Keras
We’ve mentioned how to predict the identity, emotion, age and gender with deep learning in previous posts. Ethnicity and race … More
Race and Ethnicity Prediction In The Perspective of AI Ethics
We can predict the demographic and cultural information of people including identity, age and gender, ethnicity based on their photos already. … More
Why You Should Build XGBoost Models Within H2O
XGBoost triggered the rise of the tree based models in the machine learning world. It earns reputation with its robust … More
A Gentle Introduction to XGBoost for Applied Machine Learning
XGBoost is firstly introduced in 2016 by Washington University Professors Tianqi Chen and Carlos Guestrin. Even though XGBoost appears in an … More
Artistic Style Transfer for Videos
State-of-the-art states the highest level in English. Because art is outpouring of human intelligence and sense of aesthetics. Artistic style transfer is … More
Mish As Neural Networks Activation Function
Recently, Mish activation function is announced in deep learning world. Researchers report that it overperforms than both regular ReLU and Swish. The … More
The Fastest Way to Calculate Combination and Permutation
Combination and permutation calculations appear often in daily programming challenges such as HackerRank. Even though we all know how to … More
Interpretable Machine Learning with H2O and SHAP
Previously, we’ve made explanations for h2o.ai models with lime. Lime enables questioning for made predictions of built models. Herein, SHAP … More
Explaining h2o models with Lime
Interpretability and accuracy inversely proportional concepts. Models offering higher accuracy such as deep learning or GBM would be lowly interpretable. … More
