Problem statement
Galas are the biggest party of the year. Hosting firms of these events are well aware that everyone from around the world has their eyes on these nights—be it for inspiration or for critique. It takes months of meticulous planning and delegation to host these events impeccably.
One such firm has decided to take a data-driven approach for planning their gala nights. Aesthetics and entertainment are the most crucial segments of these events. So, this firm has hired you to help them aggregate and classify all images. These images are published by attendees and the paparazzi on various social media channels and other sources. You are required to build an image auto-tagging model to classify these images into separate categories.
Dataset
The dataset consists of 5,983 images that belong to 4 categories. These categories are food, attire, decor and signage, and miscellaneous.
The benefits of practicing this problem by using Machine Learning or Deep Learning techniques are as follows:
You are required to build a model that auto-tag images and classifies them into various categories of aesthetics and entertainment for a gala night.
Overview
Deep Learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Deep Learning is a science that determines patterns in data. These patterns provide deeper meaning to problems, help you to understand problems better, and solve these problems efficiently. HackerEarth’s Deep Learning challenge is designed to help you improve your Deep Learning skills by competing and learning from fellow participants.
Here’s presenting HackerEarth’s Deep Learning Challenge—Auto-tag Images of the Gala.
This challenge is designed to help you improve your Machine Learning and Deep Learning skills by competing and learning from fellow participants.
Why should you participate?
Who should participate?
Tutorials
To know more about Machine Learning techniques, click here.
Note