Comparing Differently Trained Models At the end of the previous post, we mentioned that the solution found by L-BFGS made different errors compared to the model we trained with SGD and momentum. So, one question is what solution is better in terms of…

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…

The Evolved Transformer - Enhancing Transformer with Neural Architecture Search 2019-03-26 19:14:33   Paper:"The Evolved Transformer." So, David R., Chen Liang, and Quoc V. Le.  arXiv preprint arXiv:1901.11117 (2019).  Code: https://github.com/t…

Ensemble Methods for Deep Learning Neural Networks to Reduce Variance and Improve Performance 2018-12-19 13:02:45 This blog is copied from: https://machinelearningmastery.com/ensemble-methods-for-deep-learning-neural-networks/ Deep learning neural ne…

We strongly recommend that you pick either Keras or PyTorch. These are powerful tools that are enjoyable to learn and experiment with. We know them both from the teacher’s and the student’s perspective. Piotr has delivered corporate workshops on both…

原文: https://pgaleone.eu/tensorflow/go/2017/05/29/understanding-tensorflow-using-go/ Tensorflow is not a Machine Learning specific library, instead, is a general purpose computation library that represents computations with graphs. Its core is impleme…

How to handle Imbalanced Classification Problems in machine learning? from:https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/ Introduction If you have spent some time in machine learning and data science, you would have d…

几个有助于加深对反向传播算法直观理解的网页,包括普通前向神经网络,卷积神经网络以及利用BP对一般性函数求导 A Visual Explanation of the Back Propagation Algorithm for Neural Networks By Sebastian Raschka, Michigan State University. Let's assume we are really into mountain climbing, and to add a little e…

Abstract We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (eg, syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e. to model polysemy). 我们引入了一种新…

论文地址 Abstract Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its sense). 开放文本语义分析器被设计为通过推断相应的意义表示(MR -其意义的正式表示)来解释自然语言中的任何语句.…

我们将开始深入了解如何使用Azure机器学习的基本功能,帮助您开始迈向Azure机器学习的数据科学家之路. Azure ML Studio (Azure Machine Learning Studio / Azure ML Studio)是使用Azure机器学习云实现预测分析解决方案的主要工具.Azure机器学习是基于云计算和自容式的强大预测分析解决方案,具有完整的开发.测试和生产环节快速创建的独立闭环. Azure ML Studio提供交互式和可视化的工具轻松构建.测试和迭代预测分析模型.您…

python版本的faster-rcnn见我的另一篇博客: py-faster-rcnn(running the demo): ubuntu14.04+caffe+cuda7.5+cudnn5.1.3+python2.7环境搭建记录 1. 首先需要配置编译caffe的环境,并降级gcc为4.7.见: ubuntu14.04下安装cudnn5.1.3,opencv3.0,编译caffe及matlab和python接口过程记录(不好意思,这也是我自己写的) 2. clone 源码: git clon…

How much training data do you need?   //@樵夫上校: 0. 经验上,10X规则(训练数据是模型参数量的10倍)适用与大多数模型,包括shallow network. 1.线性模型可以应用10X的经验规则,模型参数是特征选择后的数量(PCA等方法).2.NN可以将10X规则当做训练数据量的下限. The quality and amount of training data is often the single most dominant factor t…

Applied Deep Learning Resources A collection of research articles, blog posts, slides and code snippets about deep learning in applied settings. Including trained models and simple methods that can be used out of the box. Mainly focusing on Convoluti…

发表于2012年 作者:Kota Yamaguchi M.Hadi Kiapour Luis E.Ortiz Tamara L.Berg 摘要:展示了一个从时装图片中解析衣服的有效方法,提供了一个一般数据库和标记时装的工具.提出一个初步尝试:使用衣服估计改进姿态估计,并展示了一个原型系统:姿态独立的视觉服装检索系统. 1:介绍 想一想:穿着茶礼.服戴着珍珠的路人,穿着定制的西装.皮鞋的银行家,还有穿法兰绒衬衫.紧身牛仔裤.搭配黑框眼镜的时尚潮人……论文中服装的选择都是这种紧密贴近社会的衣服. 识…

http://rogerioferis.com/VisualRecognitionAndSearch2014/Resources.html Source Code Non-exhaustive list of state-of-the-art implementations related to visual recognition and search. There is no warranty for the source code links below – use them at you…

本文来自于腾讯Bugly公众号(weixinBugly),未经作者同意,请勿转载,原文地址:https://mp.weixin.qq.com/s/OWD5UEiVu5JpYArcd2H9ig 作者:liujizhou 导语:在刚刚过去的WWDC上,苹果发布了Core ML这个机器学习框架.现在,开发者可以轻松的使用Core ML把机器学习功能集成到自己的应用里,让应用变得更加智能,给用户更牛逼的体验. 苹果在 iOS 5 里引入了 NSLinguisticTagger 来分析自然语言.iOS 8…

TensorRT是什么 建议先看看这篇https://zhuanlan.zhihu.com/p/35657027 深度学习 训练 部署 平常自学深度学习的时候关注的更多是训练的部分,即得到一个模型.而实际工作很大一块的工作内容集中于如何将模型部署到具体的芯片上.你自己写的模型效果是很难优于成熟的知名的模型的. 以无人驾驶为例,拍摄到图片后,芯片上的加载的模型要能够识别出图片里是什么.对自动驾驶这种场景而言,对实时性地要求是非常高的.试想,从图片输入到模型,到模型识别出图片中前方有个人花了1分钟,…

Below are some investigation resources for synthetic datasets: 1. Synthetic datasets vs. real images for computer vision algorithm evaluation? https://www.researchgate.net/post/Synthetic_datasets_vs_real_images_for_computer_vision_algorithm_evaluatio…

在说到人脸检测我们首先会想到利用Harr特征提取和Adaboost分类器进行人脸检测(有兴趣的可以去一看这篇博客第九节.人脸检测之Haar分类器),其检测效果也是不错的,但是目前人脸检测的应用场景逐渐从室内演变到室外,从单一限定场景发展到广场.车站.地铁口等场景,人脸检测面临的要求越来越高,比如:人脸尺度多变.数量冗大.姿势多样包括俯拍人脸.戴帽子口罩等的遮挡.表情夸张.化妆伪装.光照条件恶劣.分辨率低甚至连肉眼都较难区分等.在这样复杂的环境下基于Haar特征的人脸检测表现的不尽人意.随着深度学…

 重在图像的定位和检测的内容. 一张图片中只有一种给定类别标签的对象,定位则是图像中有对象框:再这些类中,每一个训练目标都有一个类和许多的图像内部对应类的位置选框. 猜想的仅是类标签,不如说它们是位置选框.正确的位置选框,代表你的结果很接近分割的准确率. 研究定位的简单有用基础的范式,就是回归. 这张图片经过一系列的处理过程,最终生成四个代表选框大小的实数,有很多不同的参数来描述选框,人们常用的是用XY坐标定位选框的左上角 .宽度和高度,还有一些ground truth(真实准确的选框),计算欧…

作者用游戏的暂停与继续聊明白了checkpoint的作用,在三种主流框架中演示实际使用场景,手动点赞. 转自:https://blog.floydhub.com/checkpointing-tutorial-for-tensorflow-keras-and-pytorch/ Checkpointing Tutorial for TensorFlow, Keras, and PyTorch This post will demonstrate how to checkpoint your trai…

前言: 论文及源代码网址: https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation 地址2: https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/installation.md 1.  一个失败的尝试 首先是跑一个例子试试,使用 https://github.com/CMU-Perceptual-Computing-Lab/openpo…

NLTK 知识整理 nltk.corpus模块自带语料 NLTK comes with many corpora, toy grammars, trained models, etc. A complete list is posted at: http://nltk.org/nltk_data/ Run the Python interpreter and type the commands: >>> import nltk >>> nltk.download() T…

WHAT I READ FOR DEEP-LEARNING Today, I spent some time on two new papers proposing a new way of training very deep neural networks (Highway-Networks) and a new activation function for Auto-Encoders (ZERO-BIAS AUTOENCODERS AND THE BENEFITS OFCO-ADAPTI…

计算 Amazon EC2:弹性虚拟机 AWS Batch:批处理计算 Amazon ECR:Docker容器管理 Amazon ECS:高度可扩展的快速容器管理服务 Amazon EKS:在AWS上运行K8s AWS Elastic Beanstalk:应用程序部署和管理 AWS Lambda:函数计算服务 Amazon Lightsail:快速启动项目所需的一切资源 AWS Serverless Application Model (AWS SAM):无服务器应用构建 AWS Serverl…

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…

在利用branch-site检测趋同进化的时候 .可以将各个趋同进化枝分别进行检测,分析的时候不去除某一趋同枝系 .在分析的时候,需要去除其他趋同枝系的影响 I have sequences of a bacteria gene each strains are strains I classify the first five , the next four and the rest which serves and clade . What is the right way to do th…

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun           Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com Abstract摘要 Deeper neural networks are more difficult to train. We present a residual learning framework to ease the traini…

https://en.wikipedia.org/wiki/Ensemble_learning Stacking Stacking (sometimes called stacked generalization) involves training a learning algorithm to combine the predictions of several other learning algorithms. First, all of the other algorithms are…