机器学习 第六讲：Generative Learning Algorithm B下载

weixin_39822095 2019-09-13 12:00:19
这一讲主要介绍 Generative Learning Algorithm 中的另外一种模型 Naive Bayes, 以及相应的扩展。
相关下载链接：//download.csdn.net/download/shinian1987/8476789?utm_source=bbsseo
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机器学习 第六讲：Generative Learning Algorithm B
这一讲主要介绍 Generative Learning Algorithm 中的另外一种模型 Naive Bayes, 以及相应的扩展。
机器学习 第五讲： Generative Learning Algorithm A
这一讲介绍 Generative Learning Algorithm， 主要介绍 Gaussian discriminant analysis。
【2】A fast learning algorithm for deep belief nets.pdf
We show how to use “complementary priors” to eliminate the explainingaway effects thatmake inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of thewake-sleep algorithm. After fine-tuning, a networkwith three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to displaywhat the associativememory has in mind.
CS229吴恩达机器学习完整版（打印版本）
该文档是cs229机器学习公开课的笔记，是我自己整理的一份打印版，并增加了目录，适合学习复习该课程的同学使用
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