Prince Grover

Prince Grover

Senior Applied Scientist at Amazon

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

AI Insights-In a Minute - Pilot

1 minute read

Published:

Short Summaries of AI Research.

In 2023, I plan to increase my capacity for consuming research papers. For better retention of all the research, I realized that it is important to have an organized way of summarizing the papers and to compile these summaries in a central location. After writing a few such paper summaries, I thought it might be a good idea to share them publicly, hoping that in the long run they might be useful to me and others.

Transfer Learning Using MXNet

30 minute read

Published:

This post provides you an easy to follow tutorial on how to “train a base neural net” on a dataset and use that pre-trained network to “transfer learn” on a different dataset using MXNet/Gluon framework.

Getting Deeper into Categorical Encodings for Machine Learning

less than 1 minute read

Published:

Jumping from simple algorithms to complex ones does not always boost performance if the feature engineering is not done right. One type of features that do not easily give away the information they contain are categorical features.

Custom Loss Functions for Gradient Boosting

less than 1 minute read

Published:

Optimize what matters — Authors: Prince Grover and Sourav Dey. This post is our attempt to summarize the importance of custom loss functions in many real-world problems — and how to implement them with the LightGBM gradient boosting package.

Evolution of Object Detection and Localization Algorithms

less than 1 minute read

Published:

Understanding recent evolution of object detection and localization with intuitive explanation of underlying concepts. Object detection is one of the areas of computer vision that is maturing very rapidly. Thanks to deep learning! Every year, new algorithms/ models keep on outperforming the previous ones.

Various Implementations of Collaborative Filtering

less than 1 minute read

Published:

We see the use of recommendation systems all around us. These systems are personalizing our web experience, telling us what to buy (Amazon), which movies to watch (Netflix), whom to be friends with (Facebook), which songs to listen (Spotify) etc.

Intuitive Interpretation of Random Forest

less than 1 minute read

Published:

For someone who thinks that random forest is a black box algorithm, this post can offer a differing opinion. I am going to cover 4 interpretation methods that can help us get meaning out of a random forest model with intuitive explanations.

Detection

FDB: Fraud Dataset Benchmark

1 minute read

Published:

Authors: Prince Grover, Zheng Li, Jianbo Liu, Jakub Zablocki, Hao Zhou, Julia Xu, Anqi Cheng. The Fraud Dataset Benchmark (FDB) is a compilation of publicly available datasets relevant to fraud detection. The FDB aims to cover a wide variety of fraud detection tasks, ranging from card not present transaction fraud, bot attacks, malicious traffic, loan risk and content moderation. The goal is to provide researchers working in the field of fraud and abuse detection a standardized set of benchmarking datasets and evaluation tools for their experiments.

12 Types of Fraud and Abuse that Everyone Should Know About

33 minute read

Published:

With constant changes in how we interact with external environment (e.g. how we shop, what media channels we consume, how we do banking, how we order food etc.), the types of fraud, scams and abuses are also emerging. People with malicious intent are always on the lookout for finding loopholes in systems and using those loopholes for personal gain. In this article, I discuss about a few types of common fraudulent, abusive and other malicious activities that individuals and organizations experience.

Foundations of a Fraud Detection System

7 minute read

Published:

In this post, I will discuss the basic foundations that companies need to be ready with in order to build their first fraud detection system. Over the next few weeks, I will build upon these foundations and add layers of engineering and scientific systems that will help create the most advanced fraud detection systems.

Different Design Frameworks for ML Based Fraud Detection

26 minute read

Published:

Some organizations are at an early stage of data collection while others have large teams of analysts who are continuously blocking fraudsters. In this post, we will discuss different ML frameworks that can be implemented depending on the stage of the organization.

detection

FDB: Fraud Dataset Benchmark

1 minute read

Published:

Authors: Prince Grover, Zheng Li, Jianbo Liu, Jakub Zablocki, Hao Zhou, Julia Xu, Anqi Cheng. The Fraud Dataset Benchmark (FDB) is a compilation of publicly available datasets relevant to fraud detection. The FDB aims to cover a wide variety of fraud detection tasks, ranging from card not present transaction fraud, bot attacks, malicious traffic, loan risk and content moderation. The goal is to provide researchers working in the field of fraud and abuse detection a standardized set of benchmarking datasets and evaluation tools for their experiments.

12 Types of Fraud and Abuse that Everyone Should Know About

33 minute read

Published:

With constant changes in how we interact with external environment (e.g. how we shop, what media channels we consume, how we do banking, how we order food etc.), the types of fraud, scams and abuses are also emerging. People with malicious intent are always on the lookout for finding loopholes in systems and using those loopholes for personal gain. In this article, I discuss about a few types of common fraudulent, abusive and other malicious activities that individuals and organizations experience.

Foundations of a Fraud Detection System

7 minute read

Published:

In this post, I will discuss the basic foundations that companies need to be ready with in order to build their first fraud detection system. Over the next few weeks, I will build upon these foundations and add layers of engineering and scientific systems that will help create the most advanced fraud detection systems.

Different Design Frameworks for ML Based Fraud Detection

26 minute read

Published:

Some organizations are at an early stage of data collection while others have large teams of analysts who are continuously blocking fraudsters. In this post, we will discuss different ML frameworks that can be implemented depending on the stage of the organization.

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