What to do when data is missing? - Part II

Let's use a Deep Autoencoder to impute missing categorical data from a dataset describing physical characteristics of mushrooms. How well can we do it? Let's try it with Keras in Python. »
Author's profile picture Venelin Valkov on data-science

TensorFlow for Hackers - Part I

Learning TensorFlow is easy and fun! Want to learn more about Machine Learning and Deep Learning in a practical and hands-on approach using TensorFlow? Let's start with a simple linear regression. »
Author's profile picture Venelin Valkov on data science

What to do when data is missing? - Part I

Yes! You've got the coolest dataset on your hard drive. Countless hours of fun are waiting for you. Except, some rows have missing values and your model might not be happy with those. But you have the perfect solution! You can just ignore them (nobody said delete them, right?)! Now, why this might not be the best idea? Let's dig deeper into data imputation using R. »
Author's profile picture Venelin Valkov on data-science

About Time - Part III

Enough chit-chat. Let's define a model that can schedule your day! »
Author's profile picture Venelin Valkov on projects

About Time - Part II

The ingredients of a better daily schedule. Can we make a scheduler that does it for you? »
Author's profile picture Venelin Valkov on projects