Learn Data Science using Python

Another post starts with you beautiful people! XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. Recently XGBoost is released with it's newer version 1.0.0 which has improvements like performance scaling for multi core CPUs, improved installation experience on Mac OSX, availability of distributed XGBoost on Kubernates etc. In this post we are going to explore it's multi processing capabilities on a real world ml problem  Otto Group Product Classification Challenge . In the end of the post I will share my kaggle kernel link also so that you can explore my complete code. Once you go to the challenge link in Kaggle and start your kernel, first you need to enable the Internet option in the noteb

Another post starts with you beautiful people! I have two good news for all of you! First good news is that Tensorflow has released it's new version (TF 2.1) which is focused on TPUs and the most interesting thing about this release is that it now also supports Keras high level API. And second wonderful news is to help us get started Kaggle has launched a TPU Playground Challenge . This means there is no any way to stop you learning & using TPUs. In this post I am going to share you how to configure and use TPUs while solving a image classification problem. What are TPUs? You must have heard about TPU while using  Google Colab . Now Kaggle also supports this hardware accelerator. TPUs or Tensor Processing Units are hardware accelerators specialized in deep learning tasks. They were created by Google and have been behind many cutting edge results in machine learning research. Kaggle Notebooks are configured with TPU v3-8s, which is a specialized hardware with 4 du