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

• python 3
• numpy
• pandas
• scipy
• matplotlib

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 Chap1~Chap11 folder.

chap.1 : bayesian fitting	chap.4 : logistic regression	chap.5 : neural network

The contents of Pattern Recognition and Machine Learning

• chap.1 : Introduction
• chap.2 : Probability distributions
• chap.3 : Linear Models for Regression
• chap.4 : Linear Models for Classification
• chap.5 : Neural Networks
• chap.6 : Kernel Methods
• chap.7 : Sparse Kernel Machines
• chap.8 : Graphical Models
• chap.9 : Mixture Models and EM
• chap.10 : Approximate Inference
• chap.11 : Sampling Methods

Deep learning and Convolutional neural network for image recognition

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

All sources in [~/prml_python/prml] are the module file. If you want to change certain parameters (ex. iteration number, activation function in each layer for Neural Network), check the files in that directory.