MLPs (Multi-Layer Perceptrons) are great for many classification and regression tasks, but it is hard for MLPs to do classification and regression on sequences. In this code tutorial, a GRU is implemented in TensorFlow.
How can we use machine learning to predict stockprices? In this tutorial we will make Python scripts for doing sentiment analysis on Tweets and it is explained how to use it for making predictions.
As an example, suppose we had €1000,- at the first of January of 2014 and suppose we could use the algorithm which is described in this tutorial. Then it would generate €2901,- in total on the 22th of February, 2017! The total amount of money (cash + investments) is shown in the next figure:
Despite the patience you need to have, it will be worth the waiting time eventually. As mentioned in , moods in tweets are a good indication of the movement of closing prices on a stock market. In this article, we will only predict how positive or how negative a tweet is. But it turns out that this is giving predictive signals which is accurate enough for our purposes.
End 2013: I would like to lose some weight. End 2014: Maybe I should start losing some weight. End 2015: I should definitely lose some weight. A few weeks ago: I need to lose weight. At that point and bought a book and read a lot of literature about this topic. I am following a low carbs diet and this resulted into a weight loss of 6 kg in the first 3 weeks! The key points are summarized in this post. My goal is to eventual apply some machine learning algorithms on the data I am currently collecting. If you tried a diet before or if you have collected some related data, please let me know. I will use it for the machine learning post on this topic. In this post I will summarize the key points from the literature and show some visualizations of the results so far.