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.

Read more · 11 minutes# Tensorflow

## Mastering Pandas

In this course, you will learn how to use the Python Pandas. After the course, you will be able to:

- Load and transform your data
- Visualizing data using line plots, scatter plots and histograms
- Merging and storing data

The course also includes more advanced topics, such as data parallelization and aggregation.

You can see all course content under “Curriculum” on Data Blogger Courses and the first three lessons are free. The first free lesson can be found here.

(more…) Read more## Python Deep Learning tutorial: Create a GRU (RNN) in TensorFlow

MLPs (Multi-Layer Perceptrons) are great for many classification and regression tasks. However, it is hard for MLPs to do classification and regression on sequences. In this Python deep learning tutorial, a GRU is implemented in TensorFlow. Tensorflow is one of the many Python Deep Learning libraries.

By the way, another great article on Machine Learning is this article on Machine Learning fraud detection. If you are interested in another article on RNNs, you should definitely read this article on the Elman RNN.

Read more · 10 minutes## Python Deep Learning tutorial: Elman RNN implementation in Tensorflow

In this Python Deep Learning tutorial, an implementation and explanation is given for an Elman RNN. The implementation is done in Tensorflow, which is one of the many Python Deep Learning libraries.

A more modern RNN is the GRU. A GRU has less parameters to train and is therefore quite fast. An implementation in Tensorflow of the GRU can be found here.

Read more · 13 minutes