Matlab深度置信网络DBN代码解析-NeuralNetworksForMachineLearningClass:GeoffHinton的C

上传者: 38731385 | 上传时间: 2022-05-23 14:09:36 | 文件大小: 15.31MB | 文件类型: ZIP
Matlab深度置信网络DBN代码解析用于机器学习的神经网络 Geoff Hinton 的 Coursera 课程“机器学习神经网络”的 Matlab 源文件。 Geoff Hinton 于 2019 年从 Coursera 中删除了该课程,因为他觉得现在已经过时了。 然而,这些讲座仍然是对神经网络的很好的介绍,可以在 Geoff Hinton 的网站上找到。 课程大纲如下。 一、简介 为什么我们需要机器学习 什么是神经网络 一些简单的神经元模型 一个简单的学习例子 三种学习方式 2.感知器学习过程 网络架构的主要类型概述 感知器 感知器的几何视图 为什么学习有效 感知器不能做什么 3. 反向传播学习过程 学习线性神经元的权重 线性神经元的误差面 学习逻辑输出神经元的权重 反向传播算法 如何使用反向传播算法计算的导数 4. 学习词的特征向量 学习预测下一个单词 对认知科学的简要介绍 另一个改道_softmax输出函数 神经概率语言模型 处理大量可能输出的方法 5. 使用神经网络进行物体识别 为什么物体识别很困难 实现视点不变性的方法 用于手写数字识别的卷积神经网络 用于物体识别的卷积神

文件下载

资源详情

[{"title":"( 51 个子文件 15.31MB ) Matlab深度置信网络DBN代码解析-NeuralNetworksForMachineLearningClass:GeoffHinton的C","children":[{"title":"NeuralNetworksForMachineLearningClass-master","children":[{"title":"neural-networks-ex3-OptimizationAndGeneralization","children":[{"title":"a3.m <span style='color:#111;'> 14.53KB </span>","children":null,"spread":false},{"title":"data.mat <span style='color:#111;'> 22.32MB </span>","children":null,"spread":false}],"spread":true},{"title":"neural-networks-ex1-LearnPerceptronWeights","children":[{"title":"dataset2.mat <span style='color:#111;'> 608B </span>","children":null,"spread":false},{"title":"dataset3.mat <span style='color:#111;'> 696B </span>","children":null,"spread":false},{"title":"plot_perceptron.m <span style='color:#111;'> 3.33KB </span>","children":null,"spread":false},{"title":"Assignment1","children":[{"title":"plot_perceptron.m <span style='color:#111;'> 3.33KB </span>","children":null,"spread":false},{"title":"Datasets","children":[{"title":"dataset1_ancient_octave.mat <span style='color:#111;'> 782B </span>","children":null,"spread":false},{"title":"dataset2_ancient_octave.mat <span style='color:#111;'> 802B </span>","children":null,"spread":false},{"title":"dataset2.mat <span style='color:#111;'> 608B </span>","children":null,"spread":false},{"title":"dataset3.mat <span style='color:#111;'> 696B </span>","children":null,"spread":false},{"title":"dataset3_ancient_octave.mat <span style='color:#111;'> 1007B </span>","children":null,"spread":false},{"title":"dataset4.mat <span style='color:#111;'> 720B </span>","children":null,"spread":false},{"title":"dataset4_ancient_octave.mat <span style='color:#111;'> 1.06KB </span>","children":null,"spread":false},{"title":"dataset1.mat <span style='color:#111;'> 600B </span>","children":null,"spread":false}],"spread":true},{"title":"learn_perceptron.m <span style='color:#111;'> 5.89KB </span>","children":null,"spread":false}],"spread":true},{"title":"dataset4.mat <span style='color:#111;'> 720B </span>","children":null,"spread":false},{"title":"Assignment1.zip <span style='color:#111;'> 7.80KB </span>","children":null,"spread":false},{"title":"learn_perceptron.m <span style='color:#111;'> 6.14KB </span>","children":null,"spread":false},{"title":"dataset1.mat <span style='color:#111;'> 600B </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 4.31KB </span>","children":null,"spread":false},{"title":"neural-networks-ex4-RestrictedBolzmannMachine","children":[{"title":"configuration_goodness.m <span style='color:#111;'> 692B </span>","children":null,"spread":false},{"title":"a4_randomness_source.mat <span style='color:#111;'> 2.67MB </span>","children":null,"spread":false},{"title":"cd1.m <span style='color:#111;'> 1.94KB </span>","children":null,"spread":false},{"title":"a4_main.m <span style='color:#111;'> 4.44KB </span>","children":null,"spread":false},{"title":"logistic.m <span style='color:#111;'> 70B </span>","children":null,"spread":false},{"title":"a4_rand.m <span style='color:#111;'> 409B </span>","children":null,"spread":false},{"title":"a4_init.m <span style='color:#111;'> 885B </span>","children":null,"spread":false},{"title":"configuration_goodness_gradient.m <span style='color:#111;'> 1.04KB </span>","children":null,"spread":false},{"title":"log_partition.m <span style='color:#111;'> 784B </span>","children":null,"spread":false},{"title":"hidden_state_to_visible_probabilities.m <span style='color:#111;'> 720B </span>","children":null,"spread":false},{"title":"a4.zip <span style='color:#111;'> 5.05MB </span>","children":null,"spread":false},{"title":"data_set.mat <span style='color:#111;'> 22.32MB </span>","children":null,"spread":false},{"title":"permn.m <span style='color:#111;'> 5.78KB </span>","children":null,"spread":false},{"title":"visible_state_to_hidden_probabilities.m <span style='color:#111;'> 728B </span>","children":null,"spread":false},{"title":"log_part_fn.m <span style='color:#111;'> 563B </span>","children":null,"spread":false},{"title":"show_rbm.m <span style='color:#111;'> 878B </span>","children":null,"spread":false},{"title":"sample_bernoulli.m <span style='color:#111;'> 458B </span>","children":null,"spread":false},{"title":"describe_matrix.m <span style='color:#111;'> 222B </span>","children":null,"spread":false},{"title":"binary2vector.m <span style='color:#111;'> 414B </span>","children":null,"spread":false},{"title":"optimize.m <span style='color:#111;'> 1.32KB </span>","children":null,"spread":false},{"title":"extract_mini_batch.m <span style='color:#111;'> 230B </span>","children":null,"spread":false}],"spread":false},{"title":"neural-networks-ex2-PredictNextWord","children":[{"title":"raw_sentences.txt <span style='color:#111;'> 2.82MB </span>","children":null,"spread":false},{"title":"word_distance.m <span style='color:#111;'> 819B </span>","children":null,"spread":false},{"title":"sample_output.txt <span style='color:#111;'> 1.51KB </span>","children":null,"spread":false},{"title":"train.m <span style='color:#111;'> 9.31KB </span>","children":null,"spread":false},{"title":"fprop.m <span style='color:#111;'> 3.83KB </span>","children":null,"spread":false},{"title":"README.txt <span style='color:#111;'> 5.77KB </span>","children":null,"spread":false},{"title":"data.mat <span style='color:#111;'> 7.12MB </span>","children":null,"spread":false},{"title":"display_nearest_words.m <span style='color:#111;'> 922B </span>","children":null,"spread":false},{"title":"load_data.m <span style='color:#111;'> 1.13KB </span>","children":null,"spread":false},{"title":"predict_next_word.m <span style='color:#111;'> 1.26KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明