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Saupin Guillaume

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Practical Gradient Boosting: A deep dive into Gradient Boosting in Python

Practical Gradient Boosting: A deep dive into Gradient Boosting in Python

Practical Gradient Boosting is intended for students, academics, engineers, and data scientists who wish to master XGBoost, LightGBM, or CatBoost.

2022

Saupin Guillaume

Saupin Guillaume

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Towards Data Science

Transfer learning with XGBoost and PyTorch: hack Alexnet for MNIST dataset

PyTorch and XGBoost can be combined to perform transfer learning. In this article, we show how Alexnet knowledge can be transferred on…

Jul 20, 2021

Transfer learning with XGBoost and PyTorch: hack Alexnet for MNIST dataset

Jul 20, 2021

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Saupin Guillaume
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Towards Data Science

Tuning XGBoost with XGBoost: Writing your own Hyper Parameters Optimization engine

May 29, 2021

Tuning XGBoost with XGBoost: Writing your own Hyper Parameters Optimization engine

May 29, 2021

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Saupin Guillaume
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Towards Data Science

AutoML for fast Hyperparameters tuning with SMAC

Use AutoML for Finding your path in high dimensional spaces

May 16, 2021

AutoML for fast Hyperparameters tuning with SMAC

May 16, 2021

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Saupin Guillaume
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Towards Data Science

DIY XGBoost library in less than 200 lines of python

XGBoost explained as well as gradient boosting method and HP tuning by building your own gradient boosting library for decision trees.

Apr 12, 2021

DIY XGBoost library in less than 200 lines of python

Apr 12, 2021

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Saupin Guillaume
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Towards Data Science

XGBoost for Time Series: You’re gonna need a bigger boat

XGBoost is a very powerful and versatile model. Its range of application is pretty large and it has been applied successfully for solving…

Mar 22, 2021

XGBoost for Time Series: You’re gonna need a bigger boat

Mar 22, 2021

Saupin Guillaume

70 Mathematical Concepts: Polynomials - Versatile Mathematical Tools

In this second installment of my series “70 Mathematical Concepts” inspired by my book, I will delve into the topic of polynomials.

Oct 26, 2023

70 Mathematical Concepts: Polynomials - Versatile Mathematical Tools

Oct 26, 2023

Saupin Guillaume
in
Towards Data Science

Optimization: Geometrical Interpretation of the Newton-Raphson Method

An exploration of a fundamental numerical optimization technique, with a focus on its geometrical interpretation

Oct 13, 2023

Optimization: Geometrical Interpretation of the Newton-Raphson Method

Oct 13, 2023

Saupin Guillaume
in
Level Up Coding

70 Mathematical Concepts: Linearization

Unveiling 70 Mathematical Concepts with Python

Oct 10, 2023

70 Mathematical Concepts: Linearization

Oct 10, 2023

Saupin Guillaume
in
Towards Data Science

XGBoost: How Deep Learning Can Replace Gradient Boosting and Decision Trees — Part 2: Training

A world without if

Sep 21, 2023

XGBoost: How Deep Learning Can Replace Gradient Boosting and Decision Trees — Part 2: Training

Sep 21, 2023

Saupin Guillaume
in
Towards Data Science

XGBoost: How Deep Learning Can Replace Gradient Boosting and Decision Trees — Part 1

In this article, you will learn about rewriting decision trees using a Differentiable Programming approach, as proposed by the NODE paper…

May 29, 2023

XGBoost: How Deep Learning Can Replace Gradient Boosting and Decision Trees — Part 1

May 29, 2023

Saupin Guillaume

Saupin Guillaume

Book Author

Phd | CTO at verteego.com | Math enthusiast | Lisp Lover | Tech & Math Author | https://www.amazon.co.uk/dp/B0BJ82S916

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