课程四(Convolutional Neural Networks)，第二周（Deep convolutional models: case studies） —— 2.Programming assignments : Keras Tutorial - The Happy House (not graded)

Keras tutorial - the Happy House Welcome to the first assignment of week 2. In this assignment, you will: Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-l…

Convolutional Neural Networks: Step by Step

Andrew Ng deeplearning courese-4:Convolutional Neural Network Convolutional Neural Networks: Step by Step Convolutional Neural Networks: Application Residual Networks Autonomous driving - Car detection YOLO Face Recognition for the Happy House Art: N…

Convolutional Neural Networks: Application

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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…

课程四(Convolutional Neural Networks)，第四周（Special applications: Face recognition & Neural style transfer） —— 2.Programming assignments：Art generation with Neural Style Transfer

Deep Learning & Art: Neural Style Transfer Welcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576). In this as…

课程四(Convolutional Neural Networks)，第一周（Foundations of Convolutional Neural Networks） —— 3.Programming assignments：Convolutional Model: application

Convolutional Neural Networks: Application Welcome to Course 4's second assignment! In this notebook, you will: Implement helper functions that you will use when implementing a TensorFlow model Implement a fully functioning ConvNet using TensorFlow (…

课程四(Convolutional Neural Networks)，第一周（Foundations of Convolutional Neural Networks） —— 2.Programming assignments：Convolutional Model: step by step

Convolutional Neural Networks: Step by Step Welcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagati…

[C4W2] Convolutional Neural Networks - Deep convolutional models: case studies

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ImageNet Classification with Deep Convolutional Neural Networks（译文）转载

ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton 摘要我们训练了一个大型的深度卷积神经网络,来将在ImageNet LSVRC-2010大赛中的120万张高清图像分为1000个不同的类别.对测试数据,我们得到了top-1误差率37.5%,以及top-5误差率17.0%,这个效果比之前最顶尖的都要好得多.该神经网络有…

AlexNet论文翻译-ImageNet Classification with Deep Convolutional Neural Networks

ImageNet Classification with Deep Convolutional Neural Networks 深度卷积神经网络的ImageNet分类 Alex Krizhevsky University of Toronto 多伦多大学 kriz@cs.utoronto.ca Ilya Sutskever University of Toronto 多伦多大学 ilya@cs.utoronto.ca Geoffrey E. Hinton University of Toront…

中文版 ImageNet Classification with Deep Convolutional Neural Networks

ImageNet Classification with Deep Convolutional Neural Networks 摘要我们训练了一个大型深度卷积神经网络来将ImageNet LSVRC-2010竞赛的120万高分辨率的图像分到1000不同的类别中.在测试数据上,我们得到了top-1 37.5%, top-5 17.0%的错误率,这个结果比目前的最好结果好很多.这个神经网络有6000万参数和650000个神经元,包含5个卷积层(某些卷积层后面带有池化层)和3个全连接层,最后是一个1…

Deep learning_CNN_Review：A Survey of the Recent Architectures of Deep Convolutional Neural Networks——2019

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《ImageNet Classification with Deep Convolutional Neural Networks》剖析

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深度卷积神经网络用于图像缩放Image Scaling using Deep Convolutional Neural Networks

This past summer I interned at Flipboard in Palo Alto, California. I worked on machine learning based problems, one of which was Image Upscaling. This post will show some preliminary results, discuss our model and its possible applications to Flipboa…

Image Scaling using Deep Convolutional Neural Networks

Image Scaling using Deep Convolutional Neural Networks This past summer I interned at Flipboard in Palo Alto, California. I worked on machine learning based problems, one of which was Image Upscaling. This post will show some preliminary results, dis…

Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

Understanding the Effective Receptive Field in Deep Convolutional Neural Networks 理解深度卷积神经网络中的有效感受野 Abstract摘要 We study characteristics of receptive ﬁelds of units in deep convolutional networks. The receptive ﬁeld size is a crucial issue in many vis…

Convolutional Neural Networks from deep learning (assignment 1 from week 1)

Convolutional Neural Networks https://www.coursera.org/learn/convolutional-neural-networks/home/welcome There are still something confuse me! working how to paste the jupyter here.................................................... Convolutional Neur…

《Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks》论文笔记

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[C4W4] Convolutional Neural Networks - Special applications: Face recognition & Neural style transfer

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深度学习笔记 (一) 卷积神经网络基础 (Foundation of Convolutional Neural Networks)

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一目了然卷积神经网络 - An Intuitive Explanation of Convolutional Neural Networks

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（转载）Convolutional Neural Networks卷积神经网络

Convolutional Neural Networks卷积神经网络 Contents 一:前导 Back Propagation反向传播算法网络结构学习算法二:Convolutional Neural Networks卷积神经网络三:LeCun的LeNet-5 四:CNNs的训练过程五:总结本文是我在20140822的周报,其中部分参照了以下博文或论文,如果在文中有一些没说明白的地方,可以查阅他们.对Yann LeCun前辈,和celerychen2009.zouxy09表示感谢…

Convolutional Neural Networks for Visual Recognition 2

Linear Classification 在上一讲里,我们介绍了图像分类问题以及一个简单的分类模型K-NN模型,我们已经知道K-NN的模型有几个严重的缺陷,第一就是要保存训练集里的所有样本,这个比较消耗存储空间:第二就是要遍历所有的训练样本,这种逐一比较的方式比较耗时而低效. 现在,我们要介绍一种更加强大的图像分类模型,这个模型会很自然地引申出神经网络和Convolutional Neural Networks(CNN),这个模型有两个重要的组成部分,一个是score function,将原始…

Convolutional Neural Networks 笔记

1 Foundations of Convolutional Neural Networks 1.1 cv问题图像分类.目标检测.风格转换.但是高像素的图片会带来许多许多的特征. 1.2 边缘检测(卷积操作) 图像和卷积核(滤波器)移动相乘.横向.纵向滤波器.过滤器里的值也是可以学习的. 1.3 Padding(补白) 卷积会使图像变小,丢掉部分边缘信息.所以需要将边缘补白,补充为0. 假设图片尺寸为n,卷积尺寸为f.卷积之后会变为n-f+1尺寸. padd尺寸为p.valid convolu…

[C4W1] Convolutional Neural Networks - Foundations of Convolutional Neural Networks

第一周卷积神经网络(Foundations of Convolutional Neural Networks) 计算机视觉(Computer vision) 计算机视觉是一个飞速发展的一个领域,这多亏了深度学习. 深度学习与计算机视觉可以帮助汽车,查明周围的行人和汽车,并帮助汽车避开它们. 还使得人脸识别技术变得更加效率和精准,你们即将能够体验到或早已体验过仅仅通过刷脸就能解锁手机或者门锁. 当你解锁了手机,我猜手机上一定有很多分享图片的应用.在上面,你能看到美食,酒店或美丽风景的图片. 有些…

深度学习论文翻译解析（六）：MobileNets：Efficient Convolutional Neural Networks for Mobile Vision Appliications

论文标题:MobileNets:Efficient Convolutional Neural Networks for Mobile Vision Appliications 论文作者:Andrew G.Howard Menglong Zhu Bo Chen ..... 论文地址:https://arxiv.org/pdf/1704.04861.pdf (https://arxiv.org/abs/1704.04861) 代码地址: TensorFlow官方 github-Tensorflo…

深度学习论文翻译解析（十七）：MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

论文标题:MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications 论文作者:Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam 论文地址:https://arxiv.org/abs/1704.04861…