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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Intro to RL: Environments, Agents and Heuristics
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Reinforcement Learning (RL) is a subtopic in Artificial Intelligence, which uses a more organic learning approach. It is vastly different from traditional Machine Learning and Deep Learning, which are dependent on swaths of highly prepared data. In contrast, RL models learn primarily through simulation and experience, much like humans. This article covers the very basic concepts used across RL topics.
Segmentation Tasks
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Segmentation is a popular vision problem that is concerned with analysis and object or scene understanding of the contents of a given input scene. Most commonly, this is used with images, but segmentation can also be performed on a range of datatypes such as videos or 2D and 3D point clouds. The ability of segmentation models to detect objects and identify their location makes these models incredibly useful in projects involving autonomous machines and mapping and localization.
Occupancy Grid Maps
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Occupancy grid maps are a type of representation of an environment, and is commonly used by mobile robots for tasks such as path planning, localization, tracking, and many more. This article is an introduction to occupancy grid maps.
Robot Operating System (ROS) for Beginners
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The Robot Operating System (ROS), in short, is a communication tool used in robotics to facilitate transfer of messages or data in a multi-device system. In this article, I go through the very basics of ROS and how to get started with it.
Docker for Dummies
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Docker is a useful tool widely used today to facilitate software development and shipping code. This article covers a comprehensive overview of docker containers and how to use them.
Parallel Tracking and Mapping (PTAM)
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Summary obtained from the 2007 paper Parallel Tracking and Mapping by Georg Klein and David Murray from the Department of Engineering Science, Oxford.
Deep Learning Inference Frameworks and Why We Need Them
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There are two types of deep learning frameworks.
Continuous Integration and Deployment (CI/CD)
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CI: Continuous Integration CD: Continuous Deployment CI/CD refers to an automated testing pipeline. This article provides a brief overview of the CI/CD pipeline, and frameworks that provide CI/CD services.
Overcoming Browser Cross Compatibility Issues
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From Google Chrome to Firefox to Opera GX, each browser has varying support and implementation for web pages. In this article, I explore the various challenges of developing cross-compatible webpages and some common bugfixes.
Basics of Writing Unit Test Cases
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Writing good unit test cases is an important part of any software endeavour. But what constitutes a good unit test?
Regex Cheatsheet
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This guide details the quintessential parts of regular expressions (at least for me). A more detailed list of operators can be found here.
To Do Away with Backprop
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From The Forward-Forward Algorithm: Some Preliminary Investigations, Geoffrey Hinton In this paper, Hinton explains a new forward-forward (FF) algorithm to replace backpropagation.
Genshin Impact: Console Graphics Rendering Pipeline
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In this article, I summarize highlights from this video (Speaker: Zhenzhong Yi, miHoYo Technical Lead), along with supplementary information about the various graphic rendering techniques.
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