How to use Poisson Regression Using Catboost?
Count data is everywhere! From the number of customers visiting a store to the number of goals scored in a soccer match, count data is a common type of data we encounter... Read more.
Understanding Batch Normalization and Dropout: Which is better?
In the previous post on image generation, we saw the concept of Batch Normalization and Dropout are quite frequently used in Generative AI. However, I feel there... Read more.
How to generate better images with GAN using Pytorch?
In previous post, we learnt about Variational Autoencoders (VAE) and how they can be used to reconstruct images. But, we also saw the problems with generated images.... Read more.
From Simple to Variational Autoencoders using Pytorch
In the previous post on autoencoders, we learnt that autoencoders can generate a point representation of an image. Due to which we can’t interpolate two points... Read more.
Simple guide on Autoencoders using Pytorch
Applications of Image generation is currently a hot topic in the AI Industry. Be it ghibli art or anything else, the ability to generate a non existing image is... Read more.
Simple Guide to System Design Concepts for Engineers
Introduction If you are a data scientist, ML engineer or a backend developer who likes to build models and deploying them as a service, learning the basic of designing... Read more.
Simple Time Series Data Science Case Study in Python
Introduction You can find several use cases involving time series forecasting in business these days. Be it forecasting sales, website traffic, number of customers... Read more.
Better Relevance with Pairwise Ranking in XGBoost
Introduction In pairwise ranking, the model learns the relative order of an item while considering other items in the search results. For example: In a given search... Read more.