[XAI 4] - LIME for Model Interpretability I. What is LIME? Local Interpretable Model-Agnostic Explanations (LIME) is a novel, model-agnostic, local explanation technique used for interpreting b...

Docker note I. Introduction Docker is a container management service. The keywords of Docker are develop, ship and run anywhere. The whole idea of Docker is for developers to easily develop appli...

Paper note - [Week 1] BEV-MODNet: Monocular Camera based Bird’s Eye View Moving Object Detection for Autonomous Driving [2021 IEEE Intelligent Transportation Systems Conference (ITSC)] Motivation...

[XAI 3] - Data-Centric Approaches In the previous part, we discussed various model explainability methods. Most of the methods discussed in Model Explainability Methods, are model-centric. The con...

[XAI 2] - Model Explainability Methods I. Types of model explainability methods There are different approaches that you can use to provide model explainability. Certain techniques are specific to...

[XAI 1] - Foundational Concepts of Explainability Techniques As more and more organizations have started to adopt Artificial Intelligence (AI) and Machine Learning (ML) for their critical business...

Applied Computational Thinking Problems 1. Problem 1 – Using Python to analyze historical speeches History is quite fascinating, and there are many reasons why we would look at writing algorithms...

Using Computational Thinking in Simple Challenges I. Defining the problem We are going to dive into our first scenario. You are launching a campaign for a product you’ve designed. You’re looking ...

Identifying Challenges within Solutions To learn about debugging, let’s remind ourselves that the computational thinking process is not linear. Even when we are working from the original problem, ...

Designing Solutions and Solution Processes I. Designing Solutions When we are designing solutions, we often use a design thinking model , even if we don’t always realize it. Design thinking is d...