Machine Learning Study Notes - Supervised ML Algorithms


Naive Bayes

Naive Bayes classifiers are a simple, probabilistic classifier family. These classifiers are called "Naive" because they assume that features are conditionally independent, given the class. In other words, they assume that, for all instances of a given class, the features have little/no correlation with each other.
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Machine Learning Study Notes - Evaluation and Metrics


Train-Validate-Test Design

The train-validate-test design is a very important universally applied framework for effective evaluation of machine learning models.
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Machine Learning Study Notes - Basic Concepts


  • Supervised Machine Learning: Learn to predict target values from labelled data.
    • Classification (target values are discrete classes)
    • Regression (target values are continuous values)

  • Unsupervised Machine Learning: Find structure in unlabeled data

    • Find groups of similar instances in the data (clustering)
  • Finding unusual patterns (outlier detection)
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Implementing four neural networks for multi-class text classification problems


In this post, I will show four different neural networks which could be applied to multi-class text classification problems. 

  • CNN, Convolutional Neural Networks
  • LSTM, Recurrent Neural Networks / Long Short Term Memory
  • BLSTM, Bidirectional LSTM
  • CLSTM, Convolutional LSTM

And I will implement them in Tensorflow. (GitHub repo)

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Hello World!


Code highlights test:

# This is a test
import __hello__
outstr = 'Life is short, use python.'
print(outstr)

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