A Journey into Dog Breed Classification with Deep Learning
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Original blog post: https://blog.anibalhsanchez.com/en/blogging/89-a-journey-into-dog-breed-classification-with-deep-learning.html
When I first embarked on my Udemy Data Scientist Nanodegree Program, I never anticipated it would lead me to create an algorithm capable of recognizing dog breeds from images. Coming from a background in writing a thesis on Particle Imaging and Tracking in Branched Electrochemical Systems, this project reconnected me with the fascinating field of image processing.
What the Algorithm Does - Project Highlights
The system goes beyond a simple breed classifier. The detection and recognition algorithm can:
Data-Driven Disaster Response: Smart Message Classification System
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Disaster Response Pipeline Project
Introduction
As a Udemy Data Scientist Nanodegree Program student, I'm tasked with solving the Disaster Response Pipeline Project and publishing the results.
This project aims to revolutionize disaster response by developing an intelligent system that rapidly categorizes and routes incoming messages to appropriate relief agencies. Using advanced NLP and machine learning, it provides instant multi-category classification through a user-friendly web interface, enabling swift and efficient resource allocation. The goal is to significantly improve disaster management effectiveness, ultimately saving more lives and minimizing crisis impact through data-driven response strategies.
This project applies data engineering skills to analyze disaster data from Appen and build a model for an API that classifies disaster messages. The main components include:
Answering House Prices Questions using Advanced Regression Techniques
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As a Udemy Data Scientist Nanodegree Program student, I'm tasked with writing a blog post and a kernel following the CRISP-DM process. In my blog post, I'll take a fresh approach by adhering to the CRISP-DM process to address three fundamental questions often posed in the housing markets, using the Ames dataset as a case study.
The Kaggle House Prices - Advanced Regression Techniques competition is a fantastic playground for budding data scientists like myself. It challenges us to predict house prices in Ames, Iowa, leveraging 79 predictor variables through machine learning models. This well-analyzed dataset has received over 20,000 submissions, making it an excellent resource for developing and showcasing our skills.
Objectives
In my blog post, I'll take a fresh approach by adhering to the CRISP-DM process to address three fundamental questions often posed in the housing markets, using the Ames dataset as a case study.
Tailwind CSS v3 for Joomla 4 is here!
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In December 2021, Tailwind CSS released v3, and it was a HUGE upgrade. In terms of the utility framework semantic, the upgrade included incremental improvements; but, in terms of the framework tooling, the upgrade revamped the developer experience.
To name a few of the fantastic tools that now empower Tailwind:
Standalone CLI
Now, it is possible to compile the styles with a simple command line. For instance:
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Aníbal Sánchez
Versatile Software Engineer | Full-Stack Developer (PHP, Laravel, Java, Spring, Vue.js/Vite) | Data Science Enthusiast | Open Source Contributor | Tech Entrepreneur
- PHP-Prefixer / Product Manager
- PHP-Prefixer is an automated online service powered by a complex rule-based system that applies prefixes to Composer dependencies.
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- Empower your project with our web solutions. Today, working on Laravel, Amazon AWS, and Ionic. A Joomla Volunteer.
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