When we hear the word 'Artificial Intelligence (AI)', most of us think about robots and movies like Her, Ex Machina, iRobot etc.

The mathematical side of AI is Deep Learning (or MultiLayered Neural Networks), which bolsters the learning process by extracting useful features from complex, high-dimensional data sets (e.g. images, text corpus, sound etc.).

Currently, Deep Learning is one the highest priorities for major tech companies around the world. And, this is just the beginning! In the years to come, with more data, the need for using Deep Learning models will become inevitable.

This 30-day challenge is designed to help you learn, understand, and start using Deep Learning methods on unstructured data to solve real-world problems.

Prizes

1st prize: 700 USD *
2nd prize: 500 USD *

* Cash, vouchers, or credits of an equivalent amount will be given.

Why should you participate?

To enhance your prowess in Deep Learning and gain hands-on experience
To learn how to use Deep Learning to solve problems
To work on exciting, live problems
To build your fan following in the HackerEarth community
And to win some awesome prizes!

Who should participate?

Working professionals
Machine Learning/Deep Learning enthusiasts
College students (must understand the basics of neural networks)

Tutorials

Notes

In order to be able to claim your prizes, more than 50% of your HackerEarth profile must be complete.
You must share your winning approach (exhaustive write-up) with the HackerEarth community.
The prizes will be given in the first week of October.

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Create and validate your model using the IBM Data Science Experience (DSX) platform!

IBM DSX is a powerful computational engine based on Apache Spark Executors. It has a strong computing capacity in the back end. It currently supports Python, R, and Scala.

Using IBM DSX, you can create a Python, R, or Scala, notebook-based project and create a data connection to your data source. You have options to load all types of Machine Learning algorithms that are supported by runtime from KNN and RandomForest to TensorFlow.

You can export your notebook to us for further validation after you have:

Loaded your data
Created data sets
Modeled, trained, and validated your data

For more information about IBM DSX, see https://datascience.ibm.com/.

You can log in to IBM DSX by using your IBM Bluemix credentials.