Deep learning is everywhere. From sales forecasting to segmenting skin diseases on image data — there’s nothing deep learning algorithms can’t do, given quality data. If deep learning and TensorFlow are new to you, you’re in the right place. This article will show you the entire process of... Read more

A data science model development pipeline involves various components including data injection, data preprocessing, feature engineering, feature scaling, and modeling. A data scientist needs to write the learning and inference code for all the components. The code structure sometimes becomes messier and difficult to interpret for... Read more

TLDR AUC is a good starting metric when comparing the performance of two models but it does not always tell the whole story NRI looks at the new models ability to correctly reclassify cancers and benigns and should be used alongside AUC IDI quantifies improvement of the slopes of... Read more

Decision trees are one of the most intuitive machine learning algorithms used both for classification and regression. After reading, you’ll know how to implement a decision tree classifier entirely from scratch. This is the fifth of many upcoming from-scratch articles, so stay tuned to the blog... Read more

Local Interpretable Model-agnostic Explanations (LIME) is a Python project developed by Ribeiro et al. to interpret the predictions of any supervised Machine Learning (ML) model. Most ML algorithms are black-boxes; we are unable to properly understand how they perform a specific prediction. This is a... Read more

This article is the third in a series of four articles that aim to illustrate the working of Bi-Directional Attention Flow (BiDAF), a popular machine learning model for question and answering (Q&A). To recap, BiDAF is a closed-domain, extractive Q&A model. This means that to be able to answer... Read more