Pattern Recognition and Machine Learning

PRML

These are codes implementing some algorithms introduced in "Pattern Recognition and Machine Learning" (Author: C.M.Bishop). Python language used for these implementation.

Required packages

• python 3
• numpy
• pandas
• matplotlib

Installation

1. Download the file to a local folder (e.g. ~/prml_python/) by executing:

git clone https://github.com/oilneck/prml_python.git

2. Run Python and change your directory (~/prml_python/), then run the init.py script.

3. Run some demonstration files in Sec1~Sec5 folder.

Execution example

　　 　　　

sec.1
	polynomial fitting	mean square error	bayesian fitting
sec.2	<\td>
	central limit theorem	dirichlet distribution	Nomal gamma distribution
sec.3	<\td>
	linear basis function	predictive distribution	kernel function
sec.4
	sigmoid function	binary class classification	multi class classification
sec.5
	function approximation	classification in NN	scaled conjugate method

Notebook

The contents of Pattern Recognition and Machine Learning

• sec.1 : Introduction
• sec.2 : Probability distributions
• sec.3 : Linear Models for Regression
• sec.4 : Linear Models for Classification
• sec.5 : Neural Networks

Deep learning and Convolutional neural network for image recognition

• Deep learning test 【 Required libraries : numpy, sklearn (←to fetch data) 】
• Image recognition 【 Required libraries : keras, TensorFlow, OpenCV 】

Archive

Regularization method for neural network Regularization_of_NN.pdf

Convolutional neural network CNN.pdf