Nowadays, the quality of the generated texts is becoming better and better. In this blog post, you will learn how you can use a pre-trained language model for generating Tweets. This tutorial is not only applicable to Tweets, but to any desired output task. In fact, the title of this blog post is generated by a model based on GPT-2!
(more…)Data Science
This category contains posts about data science and machine learning related topics.
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.
Opinion mining on Dutch news articles
In this blog post, I will learn you how you can mine opinions about companies from news articles. I will share how I scraped thousands of news articles in a few minutes and how one could classify the opinion expressed in the titles of the news articles. This information could be used for example to help with watching competitors of a company or to predict global trends.
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.
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.