## Combining tree based models with a linear baseline model to improve extrapolation

Combining tree based models with a linear baseline model to improve extrapolation. Writing your own sklearn functions.

## An introduction to surrogate modeling, Part III: beyond basics

A machine learning approach to accelerate engineering design. In part III, we will briefly discuss the following three trends emerged in surrogate modeling research and application

## An introduction to Surrogate modeling, Part II: case study

In part II, we will go through a case study to demonstrate how to use surrogate models in practice. The roadmap for this case study is shown below

## An introduction to surrogate modeling: fundamentals

A machine learning approach to accelerate engineering design. We will focus on the fundamentals of this method by going through the following aspects: Motivation: why do we need a method to accelerate computer simulations? Solution: how is surrogate modeling helping the situation? Details: how to actually apply surrogate modeling?

## Quick Guide to Dockerized Training in AWS Sagemaker (with Code Example)

A quick & simple guide to using Customized Docker and train your model in AWS Sagemaker! ... post explores a way of bringing your own code and image and quickly set up training in AWS Sagemaker. It comes with an easy-to-follow-along code example ...

## Using data for end-to-end microeconomic modeling

Use linear regression and Newton’s method to maximize a company’s production output. In this post I will show the usefulness of applying economic methods to a data science-like problem.

## Consequences of mistaking models for software

Consequences of mistaking models for software. In this blog, we describe the twelve ‘traps’ we face when we conflate the two and argue that we need to be cognizant of the differences and address them accordingly.

## Carryover and Shape Effects in Media Mix Modeling: Paper Review

In the following post, I’ll review and implement the main portions of “Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects”.

## How to peek inside a black box model — Understand Partial Dependence Plots

In this post, we will be learning a tool to reveal the working mechanism of a black box model. But before we start, let talk about something else.

## Deep Learning to Jump

In this short note, we describe a Jump Unit that can be used to fit a step function with a simple neural network. Our motivation comes from quantitative finance problems where discontinuities often appear.

## Machine Learning Model Deployment for Beginners

IIn this article on machine learning model deployment using serverless deployment. Serverless compute abstracts away provisioning, managing severs and configuring software, simplifying model deployment.

## Bias Correction For Paid Search In Media Mix Modeling: Paper Review

Bias Correction For Paid Search In Media Mix Modeling: Paper Review. This post provides a high-level overview of “Bias Correction For Paid Search In Media Mix Modeling”, providing code and implementation of key concepts.

## Model Lifecycle: From ideas to value

In this article, we go through the model lifecycle, from the initial conception of the idea to build models to finally delivering the value from these models.

## A Layman’s Guide to Building Your First Image Classification Model in R Using Keras

With this article, my goal is to enable you to conceptualize and build your own CNN models in R using Keras and, sequentially help to boost your confidence through hands-on coding to build even more complex models in the future using this profound API.

## The Insiders’ Guide to Generative and Discriminative Machine Learning Models

In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.

Fast loading data into Azure SQL: a lesson learned. A simple but extremely effective way to load data into Azure SQL using Apache Spark or Azure Databricks as fast as possibile.

## Data Science, Chess, and Modeling

How Chess Can Improve Your Data Science Skills. Chess and data science have a lot in common. Some seemingly surface-level parallels include imposter syndrome and a feeling of powerlessness in the face of overwhelming complexity and indecision, all on top of a time crunch.

## Classification Framework for Imbalanced Data.

Understanding and utilizing imbalanced data.This blog covers the steps involved in tackling a classification problem in imbalanced dataset. The Github repository containing all the code is available here.

## Data Cleaning and Preprocessing — Modelling Subscription for Bank Deposits

The exploration of data has always fascinated me. The kind of insights and information that can be hidden in raw data is invigorating to discover and communicate.

## The NLP Model Forge: Generate Model Code On Demand

You've seen their Big Bad NLP Database and The Super Duper NLP Repo. Now Quantum Stat is back with its most ambitious NLP product yet: The NLP Model Forge.