Thank you Jeremy and Open to Contribute

Firstly, Thank you Jeremy and Rachel for this wonderful course. The model of teaching how to do stuff and slowly introducing nitty-gritties is such a win in domains like deep learning where information on the internet is still not easily accessible to everyone. Having been on Kaggle for almost two years, I really like the way concepts are taught. And yes, spreadsheets rock!

Special thank you to you Jeremy. I am very pleased to inform that I have been able to apply the techniques you have taught in class 4 which helped me win the recommendation engine challenge on Analytics Vidhya. ( I was happy to have learnt and apply the concepts well on a new dataset and yes, it worked out quite well.

To give you certain statistics, for the recommendation engine problem statement, a single Keras-CF model would take me to 12th position. An ensemble of 5 such models would take me to 4th position. (Just as you mentioned about the 2% improvement in the class). A weighted ensemble with LightGBM is good enough for a second place and last moment teaming up gave us the win. This wouldn’t have happened without Thank you. You people are doing a fantastic job.

I’ll be happy to share the dataset if anyone is interested in working on that dataset.

Unfortunately, I couldn’t finish the exercises before Feb 27th and hence I couldn’t apply for the part-2 course. Eagerly waiting for the part-2 MOOC.

Having said that, if there is anyway we could contribute to the community, let us know. I owe you one. :slight_smile:


Congratulations on your win in this challenge!

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Thank you rodgzilla. My model was a bare-bones CF model but not a Neural Network based CF. I am still trying to figure out how to get my NN to beat the dot product based model on the validation set.

CF model? Would be great if you shared your workflow, or general approach, would be great to learn.

Great job!

I’m so pleased to hear about your win! Well done :slight_smile:

Help would be very much appreciated; in particular, I’d like to see:

  • The wiki updated with answers to common questions that come up on the forum
  • A list of errata or issues based on changes that have occured to packages since the course.

Or indeed anything else you think might be of help to the community.