【文件属性】：
文件名称：模式识别和机器学习（PRML）-Python开发
文件大小：15.56MB
文件格式：ZIP
更新时间：2021-05-25 18:21:54
Python Deep Learning
Christopher Bishop的模式识别和机器学习书的注释，代码和笔记本的存储库模式识别和机器学习（PRML）该项目旨在记录我在阅读Christopher Bishop的PRML书方面取得的进步。
它包含用于更好地理解所提出的想法的笔记本，以及指向有用论文和自制笔记的链接。
有用的链接PRML本书矩阵演算矩阵食谱PRML勘误更多PRML勘误（回购）的内容。
├──README.md────第01章│├──ch1_ex_tests.ipynb│├──第1章.ipynb│└──einsum.ipynb├──第02章│├──E
【文件预览】：
prml-master
----chapter03()
--------equivalent-kernel.ipynb(159KB)
--------sequential-bayesian-learning.ipynb(331KB)
--------ml-vs-map.ipynb(130KB)
--------evidence-approximation.ipynb(862KB)
--------predictive-distribution.ipynb(600KB)
--------linear-models-for-regression.ipynb(26KB)
--------Bayesian-linear-regression.ipynb(124KB)
----chapter05()
--------weight-space-symmetry.ipynb(5KB)
--------imgs()
--------Ellipses.ipynb(18KB)
--------soft-weight-sharing.ipynb(136KB)
--------mixture-density-networks.ipynb(719KB)
--------bayesian-neural-networks.ipynb(19KB)
--------backpropagation.ipynb(157KB)
----chapter11()
--------hybrid-montecarlo.ipynb(1.17MB)
--------gibbs-sampling.ipynb(470KB)
--------markov-chain-motecarlo.ipynb(369KB)
--------rejection-sampling.ipynb(219KB)
--------slice-sampling.ipynb(295KB)
--------transformation-random-variables.ipynb(203KB)
--------adaptive-rejection-sampling.ipynb(567KB)
----chapter12()
--------kernel-pca.ipynb(1KB)
--------bayesian-pca.ipynb(12KB)
--------probabilistic-pca.ipynb(922KB)
--------ppca.py(6KB)
--------principal-component-analysis.ipynb(147KB)
----chapter01()
--------einsum.ipynb(99KB)
--------introduction.ipynb(151KB)
--------exercises.ipynb(173KB)
----chapter09()
--------gaussian-mixture-models.ipynb(876KB)
--------mixture-of-bernoulli.ipynb(283KB)
--------k-means.ipynb(198KB)
----chapter04()
--------fisher-linear-discriminant.ipynb(151KB)
--------perceptron.ipynb(947B)
--------logistic-regression.ipynb(155KB)
--------exercises.ipynb(52KB)
--------least-squares-classification.ipynb(185KB)
----chapter14()
--------boosting.ipynb(620KB)
--------cmm-linear-regression.ipynb(324KB)
--------tree.py(7KB)
--------cmm-logistic-regression.ipynb(228KB)
--------CART.ipynb(278KB)
----chapter13()
--------linear-dynamical-system.ipynb(1KB)
--------em-hidden-markov-model.ipynb(460KB)
--------hidden-markov-model.ipynb(242KB)
----chapter06()
--------gaussian-processes.ipynb(1MB)
--------kernel-regression.ipynb(367KB)
----README.md(4KB)
----chapter10()
--------exponential-mixture-gaussians.ipynb(5KB)
--------variational-logistic-regression.ipynb(389KB)
--------mixture-gaussians.ipynb(952KB)
--------variational-univariate-gaussian.ipynb(93KB)
--------local-variational-methods.ipynb(565KB)
----chapter02()
--------exponential-family.ipynb(5KB)
--------periodic-variables.ipynb(255KB)
--------mixtures-of-gaussians.ipynb(48KB)
--------bayes-binomial.ipynb(443KB)
--------gamma-distribution.ipynb(466KB)
--------robbins-monro.ipynb(640KB)
--------Exercises.ipynb(49KB)
--------students-t-distribution.ipynb(160KB)
--------density-estimation.ipynb(1.06MB)
--------bayes-normal.ipynb(1.1MB)
----chapter07()
--------relevance-vector-machines.ipynb(811KB)
--------support-vector-machines.ipynb(1.8MB)
----misc()
--------tikz()
----.gitignore(1005B)
----chapter08()
--------img.jpeg(47KB)
--------Trees.ipynb(90KB)
--------exercises.ipynb(34KB)
--------markov-random-fields.ipynb(185KB)
--------graphical-model-inference.ipynb(40KB)
--------sum-product.ipynb(3KB)