Coursera, Deep Learning 1, Neural Networks and Deep Learning - week2, Neural Networks Basics

【DeepLearning学习笔记】Coursera课程《Neural Networks and Deep Learning》——Week2 Neural Networks Basics课堂笔记

Coursera课程<Neural Networks and Deep Learning> deeplearning.ai Week2 Neural Networks Basics 2.1 Logistic Regression as a Neutral Network 2.1.1 Binary Classification 二分类逻辑回归是一个用于二分类(binary classification)的算法.首先我们从一个问题开始说起,这里有一个二分类问题的例子,假如你有一张图片作为输入,比…

【DeepLearning学习笔记】Coursera课程《Neural Networks and Deep Learning》——Week1 Introduction to deep learning课堂笔记

Coursera课程<Neural Networks and Deep Learning> deeplearning.ai Week1 Introduction to deep learning What is a Neural Network? 让我们从一个房价预测的例子开始讲起. 假设你有一个数据集,它包含了六栋房子的信息.所以,你知道房屋的面积是多少平方英尺或者平方米,并且知道房屋价格.这时,你想要拟合一个根据房屋面积预测房价的函数. 如果使用线性回归进行拟合,那么可以拟合出一条直线.但…

课程一(Neural Networks and Deep Learning)，第四周（Deep Neural Networks） —— 3.Programming Assignments: Deep Neural Network - Application

Deep Neural Network - Application Congratulations! Welcome to the fourth programming exercise of the deep learning specialization. You will now use everything you have learned to build a deep neural network that classifies cat vs. non-cat images. In…

课程一(Neural Networks and Deep Learning)，第二周（Basics of Neural Network programming）—— 4、Logistic Regression with a Neural Network mindset

Logistic Regression with a Neural Network mindset Welcome to the first (required) programming exercise of the deep learning specialization. In this notebook you will build your first image recognition algorithm. You will build a cat classifier that r…

Neural Networks and Deep Learning

Neural Networks and Deep Learning This is the first course of the deep learning specialization at Coursera which is moderated by moderated by DeepLearning.ai. The course is taught by Andrew Ng. Introduction to deep learning Be able to explain the maj…

[C3] Andrew Ng - Neural Networks and Deep Learning

About this Course If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "s…

《Neural Networks and Deep Learning》课程笔记

Lesson 1 Neural Network and Deep Learning 这篇文章其实是 Coursera 上吴恩达老师的深度学习专业课程的第一门课程的课程笔记. 参考了其他人的笔记继续归纳的. 逻辑回归 (Logistic Regression) 逻辑回归的定义神经网络的训练过程可以分为前向传播(forward propagation) 和反向传播 (backward propagation) 的过程.我们通过逻辑回归的例子进行说明. 逻辑回归是一个用于二分类 (binary c…

第四节，Neural Networks and Deep Learning 一书小节(上)

最近花了半个多月把Mchiael Nielsen所写的Neural Networks and Deep Learning这本书看了一遍,受益匪浅. 该书英文原版地址地址:http://neuralnetworksanddeeplearning.com/ 回顾一下这本书主要讲的内容 1.使用神经网络识别手写数字作者从感知器模型引申到S型神经元.然后再到神经网络的结构.并用一个三层神经网络结构来进行手写数字识别, 作者详细介绍了神经网络学习所使用到梯度下降法,由于当训练输入数量过大时,学习过程将变…

课程四(Convolutional Neural Networks)，第二周（Deep convolutional models: case studies） —— 0.Learning Goals

Learning Goals Understand multiple foundational papers of convolutional neural networks Analyze the dimensionality reduction of a volume in a very deep network Understand and Implement a Residual network Build a deep neural network using Keras Implem…

课程一(Neural Networks and Deep Learning)，第三周（Shallow neural networks）—— 3.Programming Assignment : Planar data classification with a hidden layer

Planar data classification with a hidden layer Welcome to the second programming exercise of the deep learning specialization. In this notebook you will generate red and blue points to form a flower. You will then fit a neural network to correctly cl…

Neural Networks and Deep Learning学习笔记ch1 - 神经网络

近期開始看一些深度学习的资料.想学习一下深度学习的基础知识.找到了一个比較好的tutorial,Neural Networks and Deep Learning,认真看完了之后觉得收获还是非常多的.从最主要的感知机開始讲起.到后来使用logistic函数作为激活函数的sigmoid neuron,和非常多其它如今深度学习中常使用的trick. 把深度学习的一个发展过程讲得非常清楚,并且还有非常多源代码和实验帮助理解.看完了整个tutorial后打算再又一次梳理一遍,来写点总结.以后再看其它资料…

课程一(Neural Networks and Deep Learning)总结——2、Deep Neural Networks

Deep L-layer neural network 1 - General methodology As usual you will follow the Deep Learning methodology to build the model: 1). Initialize parameters / Define hyperparameters 2). Loop for num_iterations: a. Forward propagation b. Compute cost func…

Neural Networks and Deep Learning（week2）Logistic Regression with a Neural Network mindset（实现一个图像识别算法）

Logistic Regression with a Neural Network mindset You will learn to: Build the general architecture of a learning algorithm, including: Initializing parameters(初始化参数) Calculating the cost function and its gradient(计算代价函数,和他的梯度) Using an optimization…

[C1W3] Neural Networks and Deep Learning - Shallow neural networks

第三周:浅层神经网络(Shallow neural networks) 神经网络概述(Neural Network Overview) 本周你将学习如何实现一个神经网络.在我们深入学习具体技术之前,我希望快速的带你预览一下本周你将会学到的东西.如果在本节课中的某些细节你没有看懂你也不用担心,我们将在后面的几节课中深入讨论技术细节. 现在我们开始快速浏览一下如何实现神经网络.首先你需要输入特征 \(x\),参数 \(w\) 和 \(b\),通过这些你就可以计算出 \(z\),接下来使用 \…

课程四(Convolutional Neural Networks)，第二周（Deep convolutional models: case studies） ——3.Programming assignments : Residual Networks

Residual Networks Welcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, the…

深度学习材料：从感知机到深度网络A Deep Learning Tutorial: From Perceptrons to Deep Networks

In recent years, there’s been a resurgence in the field of Artificial Intelligence. It’s spread beyond the academic world with major players like Google, Microsoft, and Facebook creating their own research teams and making some impressive acquisition…

neural network and deep learning笔记（1）

neural network and deep learning 这本书看了陆陆续续看了好几遍了,但每次都会有不一样的收获. DL领域的paper日新月异.每天都会有非常多新的idea出来,我想.深入阅读经典书籍和paper,一定能够从中发现remian open的问题.从而有不一样的视角. PS:blog主要摘取书中重要内容简述. 摘要部分 Neural networks, a beautiful biologically-inspired programming paradigm which…

《Neural Network and Deep Learning》_chapter4

<Neural Network and Deep Learning>_chapter4: A visual proof that neural nets can compute any function文章总结(前三章翻译在百度云里) 链接:http://neuralnetworksanddeeplearning.com/chap4.html: Michael Nielsen的<Neural Network and Deep Learning>教程中的第四章主要是证明神经网络可以用…

树卷积神经网络Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning

树卷积神经网络Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning 2018-04-17 08:32:39 看_这是一群菜鸟阅读数 1906 收藏更多分类专栏: 论文解读版权声明:本文为博主原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接和本声明. 本文链接:https://blog.csdn.net/qq_24305433/article/details/79856672 一.…

Learning Combinatorial Embedding Networks for Deep Graph Matching（基于图嵌入的深度图匹配）

1. 文献信息题目: Learning Combinatorial Embedding Networks for Deep Graph Matching(基于图嵌入的深度图匹配) 作者:上海交通大学研究团队(Runzhong Wang ,Junchi Yan,Xiaokang Yang) 期刊:ICCV 2019 注:此篇论文篇幅较长,其中涉及图匹配等问题,为方便阅读,保留了较多关键信息. 2. 背景这篇论文聚焦于计算机视觉领域一项历久弥新的问题:图匹配问题.在计算机视觉中,图匹配旨在利用图…

论文阅读 TEMPORAL GRAPH NETWORKS FOR DEEP LEARNING ON DYNAMIC GRAPHS

14 TEMPORAL GRAPH NETWORKS FOR DEEP LEARNING ON DYNAMIC GRAPHS link:https://scholar.google.com.hk/scholar_url?url=https://arxiv.org/pdf/2006.10637.pdf%3Fref%3Dhttps://githubhelp.com&hl=zh-TW&sa=X&ei=oVakYtvtIo74yASQ1Jj4AQ&scisig=AAGBfm0bNv…

(转) Deep Learning Research Review Week 2: Reinforcement Learning

Deep Learning Research Review Week 2: Reinforcement Learning 转载自: https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-2-Reinforcement-Learning This is the 2nd installment of a new series called Deep Learning Resea…

[Hinton] Neural Networks for Machine Learning - Converage

Link: Neural Networks for Machine Learning - 多伦多大学 Link: Hinton的CSC321课程笔记 Ref: 神经网络训练中的Tricks之高效BP (反向传播算法) 关于梯度下降的东西,涉及的知识很多,有必要单独一章 Lecture 06 —— mini批量梯度训练及三个加速的方法 (详见链接) 一.mini-批量梯度下降概述这部分将介绍使用随机梯度下降(SGD)学习来训练NN,着重介绍mini-批量版本,而这个也是现今用的最广泛的关于训练大…

论文笔记之：Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks NIPS 2015 摘要:本文提出一种 generative parametric model 能够产生高质量自然图像.我们的方法利用 Laplacian pyramid framework 的框架,从粗到细的方式,利用 CNN 的级联来产生图像.在金字塔的每一层,都用一个 GAN,我们的方法可以产生更高分辨率的图像. 引言:在计算…

[Hinton] Neural Networks for Machine Learning - Basic

Link: Neural Networks for Machine Learning - 多伦多大学 Link: Hinton的CSC321课程笔记1 Link: Hinton的CSC321课程笔记2 一年后再看课程,亦有收获,虽然看似明白,但细细推敲其实能挖掘出很多深刻的内容:以下为在线课程以及该笔记的课程重难点总结. Lecture 01 增强学习: (这是ng的拿手好戏,他做无人直升机可是做了好久)增强学习的输出是一个动作或者一系列的动作,通过与实际的场合下的环境互动来决定动作,增强学习的…

[Hinton] Neural Networks for Machine Learning - RNN

Link: Neural Networks for Machine Learning - 多伦多大学 Link: Hinton的CSC321课程笔记补充: 参见cs231n 2017版本,ppt写得比过去更好. [译] 理解 LSTM 网络:模块内部解析讲得不错. Lecture 07 Lecture 08 完全递归网络(Fully recurrent network) Hopfield网络(Hopfield network) Elman networks and Jordan network…

[Hinton] Neural Networks for Machine Learning - Bayesian

Link: Neural Networks for Machine Learning - 多伦多大学 Link: Hinton的CSC321课程笔记 Lecture 09 Lecture 10 提高泛化能力介绍不同的方法去控制网络的数据表达能力,并介绍当我们使用这样一种方法的时候如何设置元参数,然后给出一个通过提早结束训练来控制网络能力(其实就是防止过拟合)的例子. 所以我们需要方法来阻止过拟合, 第一个方法也是目前最好的方法:就是简单的增加更多的数据,如果你能提供更多的数据,那么就不需要去提…

阅读Deep Packet Inspection based Application-Aware Traffic Control for Software Defined Networks

Deep Packet Inspection based Application-Aware Traffic Control for Software Defined Networks Globlecomm2016 核心:细化测量粒度,弥补Openflow不足,提升处理性能.丰富服务的提供: 问题:SDN中存在测量粒度不够细的问题:只测得网络状态而不能获得流量行为,OpenFlow中只能提供少量的信息,不能提供更丰富的测量信息,流分类不够细导致服务质量不够高. 所做工作:为此将DPI引入控制平面…

谣言检测(RDCL)——《Towards Robust False Information Detection on Social Networks with Contrastive Learning》

论文信息论文标题:Towards Robust False Information Detection on Social Networks with Contrastive Learning论文作者:Chunyuan Yuan, Qianwen Ma, Wei Zhou, Jizhong Han, Songlin Hu论文来源:2019,CIKM论文地址:download 论文代码:download 1 Introduction 问题:会话图中轻微的扰动讲导致现有模型的预测崩溃. 研究了两大…

DIVIDEMIX: LEARNING WITH NOISY LABELS AS SEMI-SUPERVISED LEARNING

论文阅读: DIVIDEMIX: LEARNING WITH NOISY LABELS AS SEMI-SUPERVISED LEARNING 作者说明版权声明:本文为博主原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接和本声明. 原文链接:凤尘 >>https://www.cnblogs.com/phoenixash/p/15369008.html 基本信息 \1.标题:DIVIDEMIX: LEARNING WITH NOISY LABELS AS SEMI-SUP…

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