Applications of Different Parts of an ROC Curve
Published:
Understanding the importance of different parts of an ROC curve and exploring variants of AUC for ML applications
Posts by Tags
AUC
Algorithms from Scratch
Gradient Boosting from scratch
Published:
Simplifying a complex algorithm.
CNN
Evolution of Object Detection and Localization Algorithms
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
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
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
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
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.
Gradient Boosting from scratch
Published:
Simplifying a complex algorithm.
Linear Algebra
Applications of Matrix Decompositions for Machine Learning
Published:
In this post, we’ll learn how we can solve a lot of ML problems using our old math friend: matrix decompositions. 
Loss Function
Custom Loss Functions for Gradient Boosting
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.
5 Regression Loss Functions All Machine Learners Should Know
Published:
Choosing the right loss function for fitting a model. All the algorithms in machine learning rely on minimizing or maximizing a function, which we call “objective function”.
MXNet
Transfer Learning Using MXNet
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
Applications of Matrix Decompositions for Machine Learning
Published:
In this post, we’ll learn how we can solve a lot of ML problems using our old math friend: matrix decompositions.
Metrics
Applications of Different Parts of an ROC Curve
Published:
Understanding the importance of different parts of an ROC curve and exploring variants of AUC for ML applications
5 Regression Loss Functions All Machine Learners Should Know
Published:
Choosing the right loss function for fitting a model. All the algorithms in machine learning rely on minimizing or maximizing a function, which we call “objective function”.
Network Architecture
Transfer Learning Using MXNet
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
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
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
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
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
5 Regression Loss Functions All Machine Learners Should Know
Published:
Choosing the right loss function for fitting a model. All the algorithms in machine learning rely on minimizing or maximizing a function, which we call “objective function”.
Transfer Learning
Transfer Learning Using MXNet
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
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
Applications of Matrix Decompositions for Machine Learning
Published:
In this post, we’ll learn how we can solve a lot of ML problems using our old math friend: matrix decompositions.
Various Implementations of Collaborative Filtering
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
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
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.