An introduction to this modern gradient-boosting library

If you’ve worked as a data scientist, competed in Kaggle competitions, or even browsed data science articles on the internet, there’s a high chance that you’ve heard of XGBoost. Even today, it is often the go-to algorithm for many Kagglers and data scientists working on general machine learning tasks.

While…


Using this Python library to send model training updates.

Imagine this scenario — you’re working on a deep learning project and just started a time-consuming training job on a GPU. Based on your estimates, it will take you about fifteen hours for your job to finish. Obviously, you don’t want to watch your model train for that long. …


How you can use this powerful API for analyzing articles on the web.

In the Information Age, we have a huge amount of information available to us at our fingertips. The internet is so large that actually estimating its size is a complex task. …


How to deploy your ML models quickly with this API-building tool.

Knowing how to integrate machine learning models into usable applications is an important skill for data scientists. In my previous article linked below, I demonstrated how you can quickly and easily build web apps to showcase your models with Streamlit.

However, what if you want to integrate your machine learning…


The quickest way to embed your models into web apps.

A stream in a mountain landscape with trees.

If you’re a data scientist or a machine learning engineer, you are probably reasonably confident in your ability to build models to solve real-world business problems. But how good are you at front-end web development? Can you build a visually appealing web application to showcase your models? …


Getting Started

Train, visualize, evaluate, interpret, and deploy models with minimal code

When we approach supervised machine learning problems, it can be tempting to just see how a random forest or gradient boosting model performs and stop experimenting if we are satisfied with the results. What if you could compare many different models with just one line of code? …


Understanding the hype behind this language model that generates human-like text

GPT-3 (Generative Pre-trained Transformer 3) is a language model that was created by OpenAI, an artificial intelligence research laboratory in San Francisco. The 175-billion parameter deep learning model is capable of producing human-like text and was trained on large text datasets with hundreds of billions of words.

“I am open…


Using data from high energy collisions to detect new particles

An interesting branch of physics that is still being researched and developed today is the study of subatomic particles. Scientists at particle physics laboratories around the world will use particle accelerators to smash particles together at high speeds in the search for new particles. …


Using Spotify’s data to generate music recommendations.

Have you ever wondered how Spotify recommends songs and playlists based on your listening history? Do you wonder how Spotify manages to find songs that sound similar to the ones you’ve already listened to?

Interestingly, Spotify has a web API that developers can use to retrieve audio features and metadata…


Building movie recommender systems with deep learning.

Spotlight on a stage.

In my previous article, I demonstrated how to build shallow recommender systems based on techniques such as matrix factorization using Surprise.

But what if you want to build a recommender system that uses techniques that are more sophisticated than simple matrix factorization? What if you want to build recommender systems…

Amol Mavuduru

ML Engineer and Former Researcher

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