Circuit board.

Scale out your Pandas DataFrame operations using Dask

In Pandas, one can easily apply operations on all the data using the apply method. However, this method is quite slow and is not useful when scaling up your methods. Is there a way to speed up these operations? And if so, how? Yes, there is! This blog post will explain how you can use Dask to maximize the power of parallelization and to scale out your DataFrame operations.

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Chainer.

Hands-on: Creating Neural Networks using Chainer

This tutorial is a practical guide which helps you to create Neural Networks in Chainer. The focus is not on the architecture of the networks (more about Neural Network architectures is found in this post), but it is focused on creating a pipeline. We will take a simple classification problem as an example and create the pipeline for training and testing the network and how to evaluate the model.

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Seq2Seq.

Create a Character-based Seq2Seq model using Python and Tensorflow

In this article, I will share my findings on creating a character-based Sequence-to-Sequence model (Seq2Seq) and I will share some of the results I have found. All of this is just a tiny part of my Master Thesis and it took quite a while for me to learn how to convert the theoretical concepts into practical models. I will also share the lessons that I have learned.

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