PRML-master
所属分类：人工智能/神经网络/深度学习
开发工具：Python
文件大小：2263KB
下载次数：5
上传日期：2018-12-13 10:55:04
上 传 者：
monne
说明： 模式识别与机器学习 代码 prml code
(machine learning code)
文件列表:
LICENSE (1061, 2018-04-13)
__MACOSX (0, 2018-10-14)
._LICENSE (212, 2018-04-13)
test (0, 2018-04-13)
test\nn (0, 2018-04-13)
test\nn\linalg (0, 2018-04-13)
test\nn\linalg\trace.py (788, 2018-04-13)
test (0, 2018-10-14)
test\nn (0, 2018-10-14)
test\nn\linalg (0, 2018-10-14)
test\nn\linalg\._trace.py (212, 2018-04-13)
test\nn\linalg\det.py (624, 2018-04-13)
test\nn\linalg\._det.py (212, 2018-04-13)
test\nn\linalg\inv.py (787, 2018-04-13)
test\nn\linalg\._inv.py (212, 2018-04-13)
test\nn\linalg\logdet.py (641, 2018-04-13)
test\nn\linalg\._logdet.py (212, 2018-04-13)
test\nn\linalg\cholesky.py (749, 2018-04-13)
test\nn\linalg\._cholesky.py (212, 2018-04-13)
test\nn\linalg\__init__.py (0, 2018-04-13)
test\nn\linalg\.___init__.py (212, 2018-04-13)
test\nn\linalg\solve.py (761, 2018-04-13)
test\nn\linalg\._solve.py (212, 2018-04-13)
test\nn\._linalg (212, 2018-04-13)
test\nn\array (0, 2018-04-13)
test\nn\array\flatten.py (524, 2018-04-13)
test\nn\array (0, 2018-10-14)
test\nn\array\._flatten.py (212, 2018-04-13)
test\nn\array\reshape.py (479, 2018-04-13)
test\nn\array\._reshape.py (212, 2018-04-13)
test\nn\array\__init__.py (0, 2018-04-13)
test\nn\array\.___init__.py (212, 2018-04-13)
test\nn\array\transpose.py (698, 2018-04-13)
test\nn\array\._transpose.py (212, 2018-04-13)
test\nn\array\split.py (549, 2018-04-13)
test\nn\array\._split.py (212, 2018-04-13)
test\nn\array\broadcast.py (471, 2018-04-13)
test\nn\array\._broadcast.py (212, 2018-04-13)
... ...
# PRML
Python codes implementing algorithms described in Bishop's book "Pattern Recognition and Machine Learning"
## Required Packages
- python 3
- numpy
- scipy
- jupyter (optional: to run jupyter notebooks)
- matplotlib (optional: to plot results in the notebooks)
- sklearn (optional: to fetch data)
## Notebooks
- [ch1. Introduction](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch01_Introduction.ipynb)
- [ch2. Probability Distributions](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch02_Probability_Distributions.ipynb)
- [ch3. Linear Models for Regression](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch03_Linear_Models_for_Regression.ipynb)
- [ch4. Linear Models for Classification](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch04_Linear_Models_for_Classfication.ipynb)
- [ch5. Neural Networks](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch05_Neural_Networks.ipynb)
- [ch6. Kernel Methods](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch06_Kernel_Methods.ipynb)
- [ch7. Sparse Kernel Machines](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch07_Sparse_Kernel_Machines.ipynb)
- [ch9. Mixture Models and EM](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch09_Mixture_Models_and_EM.ipynb)
- [ch10. Approximate Inference](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch10_Approximate_Inference.ipynb)
- [ch11. Sampling Methods](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch11_Sampling_Methods.ipynb)
- [ch12. Continuous Latent Variables](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/master/notebooks/ch12_Continuous_Latent_Variables.ipynb)
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