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…
How to Convert MatLab Models To Keras
Transfer learning triggered spirit of sharing among machine learning practitioners. However, they are working with really different tools. PyTorch, Caffe2,…
How SHAP Can Keep You From Black Box AI
Machine learning interpretability and explainable AI are hot topics nowadays in the data world. Just working of a model will…
