ImageNet 是一个超过15 million的图像数据集,大约有22,000类. 是由李飞飞团队从2007年开始,耗费大量人力,通过各种方式(网络抓取,人工标注,亚马逊众包平台)收集制作而成,它作为论文在CVPR-2009发布.当时人们还很怀疑通过更多数据就能改进算法的看法. 深度学习发展起来有几个关键的因素,一个就是庞大的数据(比如说ImageNet),一个是GPU的出现.(还有更优的深度模型,更好的优化算法,可以说数据和GPU推动了这些的产生,这些产生继续推动深度学习的发展). ILSV

Large Scale Visual Recognition Challenge 2015 (ILSVRC2015) Legend: Yellow background = winner in this task according to this metric; authors are willing to reveal the method White background = authors are willing to reveal the method Grey background

Adit Deshpande CS Undergrad at UCLA ('19) Blog About The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) Introduction Link to Part 1Link to Part 2 In this post, we’ll go into summarizing a lot of the new and important develo

Convolutional Neural Networks are great: they recognize things, places and people in your personal photos, signs, people and lights in self-driving cars, crops, forests and traffic in aerial imagery, various anomalies in medical images and all kinds

卷积神经网络(Convolutional neural networks,CNNs)来源于对大脑视觉皮层的研究,并于1980s开始应用于图像识别.现如今CNN已经在复杂的视觉任务中取得了巨大成功,比如图像搜索,自动驾驶,语言自动分类等等.同时CNN也应用于了其他领域,比如语音识别和自然语言处理. 13.1 视觉皮层机理 David H. Hubel和Torsten Wiesel于1958.1959年在猫的身上做实验,给出了关于视觉皮层结构的深刻见解(作者因此与1981年获得诺贝尔生物或医学奖).

0 - ABSTRACT 在这个工作中,我们研究了卷积网络的深度对于它在大规模图像识别设置上的准确率的效果.我们的主要贡献是对使用非常小的卷积核(3×3)来增加深度的网络架构进行彻底评估,这说明了通过将深度增加到16-19层能够比之前最好的业界水平更好.这些发现是我们参加ImageNet Challenge 2014的基础,在这个比赛中我们团队在定位和分类追踪任务上分别获得了第一和第二的名次.我们也表明了我们的模型可以很好的扩展到其他数据集上,并都达到了最佳的水平.我们已经将我们的两个效果最好的

[原创 深度学习与TensorFlow 动手实践系列 - 4]第四课:卷积神经网络 - 高级篇 提纲: 1. AlexNet:现代神经网络起源 2. VGG:AlexNet增强版 3. GoogleNet:多维度识别 4. ResNet:机器超越人类识别 5. DeepFace:结构化图片的特殊处理 6. U-Net:图片生成网络 7. 实例:剖析VGG,用模型进行模型参数可视化,特征提取,目标预测 期待目标: 1. 掌握AlexNet结构特点,神经网络各层之间特征传导关系,模型参数总数计算 2

#!/usr/bin/env python # Copyright 2016 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License

CS231n简介 CS231n的全称是CS231n: Convolutional Neural Networks for Visual Recognition,即面向视觉识别的卷积神经网络.该课程是斯坦福大学计算机视觉实验室推出的课程.需要注意的是,目前大家说CS231n,大都指的是2016年冬季学期(一月到三月)的最新版本. 课程描述:请允许我们引用课程主页上的官方描述如下. 计算机视觉在社会中已经逐渐普及,并广泛运用于搜索检索.图像理解.手机应用.地图导航.医疗制药.无人机和无人驾驶汽车等领

Deep history 深度学习的历史 The roots of deep learning reach back further than LeCun’s time at Bell Labs. He and a few others who pioneered the technique were actually resuscitating a long-dead idea in artificial intelligence. 深度学习的研究之根是在LeCun在Bell实验室研究之前的.

Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan[‡] & Andrew Zisserman[§] Visual Geometry Group, Department of Engineering Science, University of Oxford {karen,az}@robots.ox.ac.uk 用于大规模图像识别的深度卷积网络 Karen Simonyan[‡] &am

Table of Contents: Architecture Overview ConvNet Layers Convolutional Layer Pooling Layer Normalization Layer Fully-Connected Layer Converting Fully-Connected Layers to Convolutional Layers ConvNet Architectures Layer Patterns Layer Sizing Patterns C

前言 近年来,随着 Google 的 AlphaGo 打败韩国围棋棋手李世乭之后,机器学习尤其是深度学习的热潮席卷了整个IT界.所有的互联网公司,尤其是 Google 微软,百度,腾讯等巨头,无不在布局人工智能技术和市场.百度,腾讯,阿里巴巴,京东,等互联网巨头甚至都在美国硅谷大肆高薪挖掘人工智能人才.现在在北京,只要是机器学习算法岗位,少则月薪 20k,甚至100k 以上-- 不错,新时代时代来了,我们从互联网走向移动互联网,现在又从移动互联网走向人工智能时代.业内有人称这一次的人工智能爆发是

An end to end implementation of a Machine Learning pipeline SPANDAN MADAN Visual Computing Group, Harvard University Computer Science and Artificial Intelligence Laboratory, MIT   Link to Github Repo   Section 1. Introduction Background In the fall o

http://cs231n.github.io/   里面有很多相当好的文章 http://cs231n.github.io/convolutional-networks/ Table of Contents: Architecture Overview ConvNet Layers Convolutional Layer Pooling Layer Normalization Layer Fully-Connected Layer Converting Fully-Connected Laye

安装TensorFlow  包依赖 C:\Users\sas> pip3 install --upgrade tensorflow Collecting tensorflow Downloading https://files.pythonhosted.org/packages/35/f6/8af765c7634bc72a902c50d6e7664cd1faac6128e7362510b0234d93c974/tensorflow-1.7.0-cp36-cp36m-win_amd64.whl (

Convolution Fundamental I Foundations of CNNs Learning to implement the foundational layers of CNN's (pooling,convolutions) and to stack them properly in a deep network to solve multi-class image classification problems. Computer vision Computer visi

ImageNet Classification with Deep Convolutional Neural Networks 从AlexNet剖析-卷积网络CNN的一般结构 AlexNet是Hinton和他的学生Alex Krizhevsky在12年ImageNet Challenge使用的模型结构,刷新了Image Classification的几率,从此deep learning在Image这块开始一次次超过state-of-art,甚至于搭到打败人类的地步,看这边文章的过程中,发现了很多

About this Course This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good res

About this Course You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been

1.文章原文地址 Very Deep Convolutional Networks for Large-Scale Image Recognition 2.文章摘要 在这项工作中,我们研究了在大规模的图像识别数据集上卷积神经网络的深度对准确率的影响.我们主要贡献是使用非常小(3×3)卷积核的架构对深度增加的网络进行全面的评估,其结果表明将深度增大到16-19层时网络的性能会显著提升.这些发现是基于我们在ImageNet Challenge 2014的目标检测和分类任务分别获得了第一名和第二名的成