深度学习进阶:算法与应用

课程简介:

彭亮
美国犹他州立大学在读计算机博士,从事机器学习,深度学习,以及计算机视觉方向的研究。美国国家科学基金年轻学者奖学金获得者。

[code]官方课程链接:http://www.maiziedu.com/course/572/[/code]

课程目录:

1.基本概念清晰版
2.软件包安装和环境配置总述
3.环境配置分部详解
4.环境配置分部详解下
5.手写数字识别
6.神经网络基本结构及梯度下降算法
7.随机梯度下降算法
8.梯度下降算法实现上
9.梯度下降算法实现下
10.神经网络手写数字演示
11.Backpropagation算法上
12.Backpropagation算法下
13.Backpropagation算法实现
14.cross-entropy函数
15.Softmax和Overfitting
16.Regulization
17.Regulazition和Dropout
18.正态分布和初始化(修正版)
19.提高版本的手写数字识别实现
20.神经网络参数hyper-parameters选择
21.深度神经网络中的难点
22.用ReL解决VanishingGradient问题
23.ConvolutionNerualNetwork算法
24.ConvolutionNeuralNetwork实现上
25.ConvolutionNeuralNetwork实现下
26.Restricted Boltzmann Machine
27.Restricted Boltzmann Machine下
28.Deep Brief Network 和 Autoencoder

课程截图:

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