supervised learning

In the previous two posts, I have shared basic concepts and useful functions of text mining and NLP. In this third post of text mining in Python, we finally proceed to the advanced part of text mining, that is, to build text classification model. In this post I will share the main tasks of text classification. Two useful classification models, their implementation in Python and methods of improving classification performance.

Intuitively, simple neural network is a combination of many (linear) transformations, which is similar to mixture model in some way. It allows to transform the input data in a more sophisticated way that a single linear model could not achieve. Simple neural network is the foundation for many other more advanced neural network models e.g., Recurrent Neural Network and Long Short Term Memory (LSTM). By the way, I posted a project of LSTM here please feel free to check it out if you are interested.

The content of this post includes:

1. The basics of feedforward neural network.
2. The application of it with the help of TensorFlow and Keras.
3. Several useful parameter tunings.

The main reference is Deep Learning, Goodfellow et al, Chapter 6.

This project is focused on solving the question: Is it possible to let the machine evaluate a wine like a sommelier?
The answer is yes! With the help of simple Neural Network and Long short-term memory(LSTM), we can make it possible.