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HackerEarth is home to over 2 million developers. Almost a year ago, HackerEarth started its deep learning challenge series to help developers learn and apply deep learning concepts to real-world datasets. Deep Learning challenge #1 challenged the machine learning community to build an image classifier that can predict the category to which a product belongs. Deep Learning challenge #2 challenged the developers to build prediction models that can predict the class of thoracic disease from a given chest X-ray image. Deep Learning challenge #3 encouraged participants to build predictive models that can identify all the attributes associated with an animal through the animal’s image.

Now, with Deep Learning Challenge #4, we want to step up the game by exposing one of the HackerEarth problems for this challenge. HackerEarth wants to improve the developers and customers experience by suggesting relevant tags based on an idea submitted by a participant during a hackathon. In this challenge, we challenge you to build a model that can predict relevant tags related to the inputted article.

======================Win Prizes from Amazon======================

Apart from the challenge prize money, Amazon will provide prizes worth $700 to the winners if they use Sagemaker to build their models.

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. 

Amazon SageMaker makes it easy to build ML models and get them ready for training by providing everything you need to quickly connect to your training data, and to select and optimize the best algorithm and framework for your application. Amazon SageMaker includes hosted Jupyter notebooks that make it easy to explore and visualize your training data stored in Amazon S3. You can connect directly to data in S3, or use AWS Glue to move data from Amazon RDS, Amazon DynamoDB, and Amazon Redshift into S3 for analysis in your notebook.You can begin training your model with a single click in the Amazon SageMaker console. To make the training process even faster and easier, Amazon SageMaker can automatically tune your model to achieve the highest possible accuracy. Amazon SageMaker takes away the heavy lifting of machine learning, so you can build, train, and deploy machine learning models quickly and easily.

Note: Please fill the google form on the problem statement page to request AWS Sagemaker credits.


Why should you participate?

  • To learn and use the latest open-source libraries and packages
  • To learn by working on live problems because it excites you more than learning from books and tutorials!
  • To build your fan following in our community
  • Of course, grab cash prizes

Who should participate?

  • Working professionals
  • Data Science/Machine Learning enthusiasts
  • College students (if you understand the basics of predictive modeling)



  • In order to be able to claim your prizes, your HackerEarth profile must be more than 50% complete.
  • Submit the source code files before the challenge ends.
  • The prizes will be disbursed at the end of December.


There are great prizes to be won

1st Prize

USD 700 USD 

2nd Prize

USD 500 


  1. When the contest is running, your output will be evaluated only for 50% of the test data. After the contest is over, your output for the remaining 50% of the test data wi...


Sample challenge

1. Can I participate in a sample challenge?

Yes, we recommend that you participate in our sample challenge.This challenge enables you to understand how to ...

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