Posts by Tags

AUC

Algorithms from Scratch

CNN

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.

Catbost

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.

Collaborative Filtering

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.

Gradient Boosting

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.

Linear Algebra

Loss Function

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.

MXNet

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.

Matrix

Metrics

Network Architecture

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.

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.

Object Detection

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.

Random Forest

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.

Recommendation System

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.

Regression

Transfer Learning

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.

Youtube

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.

fast.ai

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.

t-SNE

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.