Popular Deep Learning Tools – a review Deep Learning is the hottest trend now in AI and Machine Learning. We review the popular software for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch. comments By Ran B…
In this post, I review the literature on semantic segmentation. Most research on semantic segmentation use natural/real world image datasets. Although the results are not directly applicable to medical images, I review these papers because research o…
文章链接:https://arxiv.org/pdf/1509.06451.pdf 1.关于人脸检测的一些小小总结(Face Detection by Literature) (1)Multi-view Face Detection Using Deep Convolutional Neural Network Train face classifier with face (> 0.5 overlap) and background (<0.5 overlap) images. Comput…
Image Registration is a fundamental step in Computer Vision. In this article, we present OpenCV feature-based methods before diving into Deep Learning. What is Image Registration? Image registration is the process of transforming different images of…
http://blog.revolutionanalytics.com/2016/08/deep-learning-part-1.html Deep Learning Part 1: Comparison of Symbolic Deep Learning Frameworks by Anusua Trivedi, Microsoft Data Scientist Background and Approach This blog series is based on my upcoming t…
HOME ABOUT CONTACT SUBSCRIBE VIA RSS DEEP LEARNING FOR ENTERPRISE Distributed Deep Learning, Part 1: An Introduction to Distributed Training of Neural Networks Oct 3, 2016 3:00:00 AM / by Alex Black and Vyacheslav Kokorin Tweet inShare27 This pos…
In the last chapter we learned that deep neural networks are often much harder to train than shallow neural networks. That's unfortunate, since we have good reason to believe that if we could train deep nets they'd be much more powerful than shallow…
Deep Learning 方向的部分 Paper ,自用.一 RNN 1 Recurrent neural network based language model RNN用在语言模型上的开山之作 2 Statistical Language Models Based on Neural Networks Mikolov的博士论文,主要将他在RNN用在语言模型上的工作进行串联 3 Extensions of Recurrent Neural Network Language Model 开山之…
Deep Learning Methods for Vision CVPR 2012 Tutorial 9:00am-5:30pm, Sunday June 17th, Ballroom D (Full day) Rob Fergus (NYU), Honglak Lee (Michigan), Marc'Aurelio Ranzato (Google) Ruslan Salakhutdinov(Toronto), Graham Taylor(Guelph), Kai Yu(Baidu) O…
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…
The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near July 27, 2015July 27, 2015 Tim Dettmers Deep Learning, NeuroscienceDeep Learning, dendritic spikes, high performance computing, neuroscience, singula…
转自:机器学习(Machine Learning)&深度学习(Deep Learning)资料 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最…
A Statistical View of Deep Learning (III): Memory and Kernels Memory, the ways in which we remember and recall past experiences and data to reason about future events, is a term used frequently in current literature. All models in machine learning co…
Introduction Deep learning is a recent trend in machine learning that models highly non-linear representations of data. In the past years, deep learning has gained a tremendous momentum and prevalence for a variety of applications (Wikipedia 2016a).…
[原文链接] Background removal with deep learning This post describes our work and research on the greenScreen.AI. We’ll be happy to hear thoughts and comments! Intro Throughout the last few years in machine learning, I’ve always wanted to build real ma…
A Survey of Visual Attention Mechanisms in Deep Learning 2019-12-11 15:51:59 Source: Deep Learning on Medium Visual Glimpses and Reinforcement Learning The first paper we will look at is from Google’s DeepMind team: “ Recurrent Models of Visual Atten…
Research Guide for Video Frame Interpolation with Deep Learning This blog is from: https://heartbeat.fritz.ai/research-guide-for-video-frame-interpolation-with-deep-learning-519ab2eb3dda In this research guide, we’ll look at deep learning papers aime…
<Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最新版本<神经网络与深度学习综述>本综述的特点是以时间排序,从1940年开始讲起,到60-80…
