studyliuup / Practice-of-Machine-Learning

code of scattered practices when studying "machine-learning".

Practice of MachineLearning（机器学习练习的一些代码托管）

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here is a repository of scattered studies of "machine-learning".

for more info, welcome to my blog: Yuan's Homepage or Snoopy_Yuan的博客

List

• code; Kaggle-Avazu-CTR-Prediction experiments based on LR、GBDT、GBDT-LR using sklearn and FM、FFM using xlearn.(python code here).
• code; Kaggle-Titanic experiments based on Decision Tree using sklearn.(python code here).
• code; MNIST experiments based on Softmax, MLP, CNN using Tensorflow.(python code here).
• code; ensemble learning experiment based on RF(Random Forest) and GBDT(Gradient Boosting Decision Tree) using sklearn.(python code here).
• code; MNIST experiment based on CNN (Convolutional Neural Networks) using Theano.(python code here).
• code; MNIST experiment based on MLP (Multilayer Perceptron) using Theano.(python code here).
• code; implementation of standard BP algorithm.(python code here).
• code; implementation practice of BP network based on PyBrain.(python code here)
• note; parameter learning of Bayesian Network(BN).