prml：Christopher Bishop的《模式识别和机器学习》一书的注释，代码和笔记本的存储库-源码

[{"title":"（ 72 个子文件 14.13MB ） prml：Christopher Bishop的《模式识别和机器学习》一书的注释，代码和笔记本的存储库-源码","children":[{"title":"prml-master","children":[{"title":"chapter03","children":[{"title":"equivalent-kernel.ipynb <span style='color:#111;'> 158.77KB </span>","children":null,"spread":false},{"title":"sequential-bayesian-learning.ipynb <span style='color:#111;'> 331.13KB </span>","children":null,"spread":false},{"title":"evidence-approximation.ipynb <span style='color:#111;'> 862.21KB </span>","children":null,"spread":false},{"title":"predictive-distribution.ipynb <span style='color:#111;'> 599.78KB </span>","children":null,"spread":false},{"title":"linear-models-for-regression.ipynb <span style='color:#111;'> 25.86KB </span>","children":null,"spread":false},{"title":"Bayesian-linear-regression.ipynb <span style='color:#111;'> 123.75KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter05","children":[{"title":"weight-space-symmetry.ipynb <span style='color:#111;'> 4.58KB </span>","children":null,"spread":false},{"title":"imgs","children":[{"title":"f51.png <span style='color:#111;'> 123.57KB </span>","children":null,"spread":false}],"spread":true},{"title":"Ellipses.ipynb <span style='color:#111;'> 18.00KB </span>","children":null,"spread":false},{"title":"soft-weight-sharing.ipynb <span style='color:#111;'> 136.35KB </span>","children":null,"spread":false},{"title":"mixture-density-networks.ipynb <span style='color:#111;'> 718.51KB </span>","children":null,"spread":false},{"title":"bayesian-neural-networks.ipynb <span style='color:#111;'> 18.67KB </span>","children":null,"spread":false},{"title":"backpropagation.ipynb <span style='color:#111;'> 156.72KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter11","children":[{"title":"hybrid-montecarlo.ipynb <span style='color:#111;'> 1.17MB </span>","children":null,"spread":false},{"title":"gibbs-sampling.ipynb <span style='color:#111;'> 470.23KB </span>","children":null,"spread":false},{"title":"markov-chain-motecarlo.ipynb <span style='color:#111;'> 368.55KB </span>","children":null,"spread":false},{"title":"rejection-sampling.ipynb <span style='color:#111;'> 219.10KB </span>","children":null,"spread":false},{"title":"slice-sampling.ipynb <span style='color:#111;'> 295.21KB </span>","children":null,"spread":false},{"title":"transformation-random-variables.ipynb <span style='color:#111;'> 202.57KB </span>","children":null,"spread":false},{"title":"adaptive-rejection-sampling.ipynb <span style='color:#111;'> 567.14KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter12","children":[{"title":"kernel-pca.ipynb <span style='color:#111;'> 1.19KB </span>","children":null,"spread":false},{"title":"bayesian-pca.ipynb <span style='color:#111;'> 11.59KB </span>","children":null,"spread":false},{"title":"probabilistic-pca.ipynb <span style='color:#111;'> 921.76KB </span>","children":null,"spread":false},{"title":"ppca.py <span style='color:#111;'> 5.66KB </span>","children":null,"spread":false},{"title":"principal-component-analysis.ipynb <span style='color:#111;'> 147.24KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter01","children":[{"title":"einsum.ipynb <span style='color:#111;'> 99.07KB </span>","children":null,"spread":false},{"title":"introduction.ipynb <span style='color:#111;'> 150.65KB </span>","children":null,"spread":false},{"title":"exercises.ipynb <span style='color:#111;'> 173.30KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter09","children":[{"title":"gaussian-mixture-models.ipynb <span style='color:#111;'> 876.31KB </span>","children":null,"spread":false},{"title":"mixture-of-bernoulli.ipynb <span style='color:#111;'> 283.24KB </span>","children":null,"spread":false},{"title":"k-means.ipynb <span style='color:#111;'> 197.87KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter04","children":[{"title":"fisher-linear-discriminant.ipynb <span style='color:#111;'> 151.33KB </span>","children":null,"spread":false},{"title":"perceptron.ipynb <span style='color:#111;'> 947B </span>","children":null,"spread":false},{"title":"logistic-regression.ipynb <span style='color:#111;'> 155.05KB </span>","children":null,"spread":false},{"title":"exercises.ipynb <span style='color:#111;'> 52.27KB </span>","children":null,"spread":false},{"title":"least-squares-classification.ipynb <span style='color:#111;'> 184.83KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter14","children":[{"title":"boosting.ipynb <span style='color:#111;'> 619.75KB </span>","children":null,"spread":false},{"title":"cmm-linear-regression.ipynb <span style='color:#111;'> 323.67KB </span>","children":null,"spread":false},{"title":"tree.py <span style='color:#111;'> 7.34KB </span>","children":null,"spread":false},{"title":"cmm-logistic-regression.ipynb <span style='color:#111;'> 228.46KB </span>","children":null,"spread":false},{"title":"CART.ipynb <span style='color:#111;'> 277.68KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter13","children":[{"title":"linear-dynamical-system.ipynb <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"em-hidden-markov-model.ipynb <span style='color:#111;'> 459.96KB </span>","children":null,"spread":false},{"title":"hidden-markov-model.ipynb <span style='color:#111;'> 242.26KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter06","children":[{"title":"gaussian-processes.ipynb <span style='color:#111;'> 1.00MB </span>","children":null,"spread":false},{"title":"kernel-regression.ipynb <span style='color:#111;'> 366.70KB </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 4.31KB </span>","children":null,"spread":false},{"title":"chapter10","children":[{"title":"exponential-mixture-gaussians.ipynb <span style='color:#111;'> 4.90KB </span>","children":null,"spread":false},{"title":"variational-logistic-regression.ipynb <span style='color:#111;'> 389.15KB </span>","children":null,"spread":false},{"title":"mixture-gaussians.ipynb <span style='color:#111;'> 951.90KB </span>","children":null,"spread":false},{"title":"variational-univariate-gaussian.ipynb <span style='color:#111;'> 92.91KB </span>","children":null,"spread":false},{"title":"local-variational-methods.ipynb <span style='color:#111;'> 565.00KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter02","children":[{"title":"exponential-family.ipynb <span style='color:#111;'> 5.28KB </span>","children":null,"spread":false},{"title":"periodic-variables.ipynb <span style='color:#111;'> 254.57KB </span>","children":null,"spread":false},{"title":"mixtures-of-gaussians.ipynb <span style='color:#111;'> 48.03KB </span>","children":null,"spread":false},{"title":"bayes-binomial.ipynb <span style='color:#111;'> 442.93KB </span>","children":null,"spread":false},{"title":"gamma-distribution.ipynb <span style='color:#111;'> 466.39KB </span>","children":null,"spread":false},{"title":"robbins-monro.ipynb <span style='color:#111;'> 639.56KB </span>","children":null,"spread":false},{"title":"Exercises.ipynb <span style='color:#111;'> 48.69KB </span>","children":null,"spread":false},{"title":"students-t-distribution.ipynb <span style='color:#111;'> 160.17KB </span>","children":null,"spread":false},{"title":"density-estimation.ipynb <span style='color:#111;'> 1.06MB </span>","children":null,"spread":false},{"title":"bayes-normal.ipynb <span style='color:#111;'> 1.10MB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter07","children":[{"title":"RVMs.ipynb <span style='color:#111;'> 810.67KB </span>","children":null,"spread":false}],"spread":false},{"title":"misc","children":[{"title":"tikz","children":[{"title":"ch8-sum-product.tex <span style='color:#111;'> 823B </span>","children":null,"spread":false},{"title":"ch13-hmm.tex <span style='color:#111;'> 1.96KB </span>","children":null,"spread":false}],"spread":false}],"spread":false},{"title":".gitignore <span style='color:#111;'> 1005B </span>","children":null,"spread":false},{"title":"chapter08","children":[{"title":"img.jpeg <span style='color:#111;'> 46.63KB </span>","children":null,"spread":false},{"title":"Trees.ipynb <span style='color:#111;'> 90.25KB </span>","children":null,"spread":false},{"title":"exercises.ipynb <span style='color:#111;'> 33.66KB </span>","children":null,"spread":false},{"title":"markov-random-fields.ipynb <span style='color:#111;'> 184.67KB </span>","children":null,"spread":false},{"title":"graphical-model-inference.ipynb <span style='color:#111;'> 40.29KB </span>","children":null,"spread":false},{"title":"sum-product.ipynb <span style='color:#111;'> 3.46KB </span>","children":null,"spread":false}],"spread":false}],"spread":false}],"spread":true}]