Combination and permutation calculations appear often in daily programming challenges such as HackerRank. Even though we all know how to…
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…
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.…
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…
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.…
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…
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…
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…
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…
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…
