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
- Download the file to a local folder (e.g. ~/prml_python/) by executing:
git clone https://github.com/oilneck/prml_python.git-
Run Python and change your directory (~/prml_python/), then run the
init.pyscript. -
Run some demonstration files in Chap1~Chap11 folder.
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|---|---|---|
| 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.
| Wiki | Wiki for prml algorithm |
|---|---|
| Text | Regularization_of_NN.pdf |
| Slide | CNN.pdf |