Combining tree based models with a linear baseline model to improve extrapolation. Writing your own sklearn functions.
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
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
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?
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 ...
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. 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.
In the following post, I’ll review and implement the main portions of “Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects”.
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.
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.
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. This post provides a high-level overview of “Bias Correction For Paid Search In Media Mix Modeling”, providing code and implementation of key concepts.
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.
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.
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.
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.
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.
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.
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.