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Explaining black-box models using attribute importance, PDPs, and LIME

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner...

Building a Speaker Recognition Model

[mathjax] The ability of a system to recognize a person by their voice is a non-intrusive way to collect their biometric information. Unlike fingerprint detection, retinal...

Building a Named Entity Recognition model using a BiLSTM-CRF network

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus...

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. We present the inner workings of...

Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

In this article, we'll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human...

On-Demand Spark clusters with GPU acceleration

Apache Spark has become the de facto standard for processing large amounts of stationary and streaming data in a distributed fashion. The addition of the MLlib...

Choosing the right Machine Learning Framework

Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is used...

Bringing ML to Agriculture: Transforming a Millennia-old Industry

Guest post by Jeff Melching from The Climate Corporation At The Climate Corporation, we aim to help farmers better understand their operations and make better decisions...

The importance of structure, coding style, and refactoring in notebooks

Notebooks are increasingly crucial in the data scientist's toolbox. Although considered relatively new, their history traces back to systems like Mathematica and MATLAB. This form of...

Evaluating Ray: Distributed Python for Massive Scalability

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. If you are interested...

Evaluating Generative Adversarial Networks (GANs)

This article provides concise insights into GANs to help data scientists and researchers assess whether to investigate GANs further. If you are interested in a tutorial...

Data Drift Detection for Image Classifiers

This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in production....

Model Interpretability: The Conversation Continues

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Insights are drawn from Bin Yu, W....

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