最近开始看Deep Learning,随手记点,方便以后查看. 主要参考资料是Stanford 教授 Andrew Ng 的 Deep Learning 教程讲义:http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial.这个讲义已经有人翻译了(赞一个),可以参见邓侃的新浪博客http://blog.sina.com.cn/s/blog_46d0a3930101h6nf.html.另外,博客园里有一个前辈关于讲义中练习的一系列文…
摘要: 一个新的系列,来自于斯坦福德深度学习在线课程:http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial.本文梳理了该教程第一部分的内容,主要包括人工神经网络入门.反向传递算法.梯度检验与高级优化 和 自编码算法与稀疏性等要点.最后以课程作业作为总结和练习. 前言 斯坦福深度学习在线课程是 Andrew Ng 编制的,该教程以深度学习中的重要概念为线索,基本勾勒出了深度学习的框架.为了简明扼要,该教程几乎省略了数学推导和证…
Gradient checking and advanced optimization In this section, we describe a method for numerically checking the derivatives computed by your code to make sure that your implementation is correct. Carrying out the derivative checking procedure describe…
一.源代码下载 代码最初来源于Github:https://github.com/vijayvee/Recursive-neural-networks-TensorFlow,代码介绍如下:“This repository contains the implementation of a single hidden layer Recursive Neural Network.Implemented in python using TensorFlow. Used the trained mode…
Neural Networks We will use the following diagram to denote a single neuron: This "neuron" is a computational unit that takes as input x1,x2,x3 (and a +1 intercept term), and outputs , where is called the activation function. In these notes, we…
import os import torch import torch.nn as nn import torch.nn.functional as F import torchvision from torchvision import transforms from torchvision.utils import save_image # 配置GPU或CPU设置 device = torch.device('cuda' if torch.cuda.is_available() else '…
