Combination and permutation calculations appear often in daily programming challenges such as HackerRank. Even though we all know how to … More
Author: Sefik Serengil
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
A Gentle Introduction to H2O GBM
GBM dominates tabular data based kaggle challenges. Putting it in the tool box is a must for data scientist. XGBoost, LightGBM … More
H2O Frame: Calling Forth The Power of Ten Tigers
Pandas has the power of a tiger. Its performance can still surprise me. However, it comes with a huge shortage. … More
Tips for Building AutoML with AutoKeras
Previously, we’ve already mentioned AutoKeras – an AutoML tool supported by Keras team. It just handles image data in a … More
A Gentle Introduction to H2O AutoML
People always have an edge to AI because they have fear to lose their daily jobs. Herein, jobs of AI … More
A Gentle Introduction to Chefboost for Applied Machine Learning
Even though deep learning is hottest topic in the media, decision trees dominates the real world challenges. Recently, I’ve announced a … More
From Face Recognition to Kinship Prediction: A Kaggle Experience
Recently, Kaggle announced a competition aiming to find related face pairs in a random set. This was my 1st kaggle … More
Face Recognition with OpenFace in Keras
OpenFace is a lightweight and minimalist model for face recognition. Similar to Facenet, its license is free and allowing commercial … More
