With the rise of Data Science, Python is more popular than ever. Therefore, it is important to use a great IDE (Integrated Development Environment) that suits your needs. This blog post gives an overview of the most popular IDEs used in Data Science.
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.
When I first heard the term Big Data few years ago, I didn’t think much of it. Soon after, Big Data started appearing in many of my conversations with many of my tech friends. So when I met this Mr. Know It All consultant, I asked him ‘What is Big Data?’. He looked at me as if I just landed from Mars and went on to explain why Big Data is the next ‘in thing’ and why everyone should know about Big Data but never directly answered my question.
By the way, if you are interesting in data mining and medical data, you should definitely read this article.
In this machine learning fraud detection tutorial, I will elaborate how got I started on the Credit Card Fraud Detection competition on Kaggle. The goal of the task is to automatically identify fraudulent credit card transactions using Machine Learning. My Pythonic approach is explained step-by-step.
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.