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Deep Learning

Convolutions 2014 ImageNet top-5 error 6.7% Inception, network in network Inception GoogLeNet ResNet Deep Residual Learning for Image Recognition, Kaiming He et. al., MSRA ImageNet top-5 error 3.57% Residual DNN-weighted Deep Neural Networks for YouTube Recommendations 16 RecSys RNN ? Transfer Learning Segmentation end-to-end: 输入图片(smaller),输出标注图片 multi-scale approach: Farable et al. A survey on transfer learning[J].

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deep learning paper

Some high-light papers are selected just for reference, most of them are associated with machine learningdeep learning) for 3D data. (CVPR 2017) (5) Pointclouds (Classification&Segmentation&Matching) PointNet: Deep Learning on Point Sets (CVPR 2017 ) PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space – Qi et al (CVPR 2018) Dynamic Graph CNN for Learning on Point Clouds – Wang et al.

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    Deep Learning综述

    Image understanding with deep convolutional networks 直到2012年ImageNet大赛之前,卷积神经网络一直被主流机器视觉和机器学习社区所遗弃。 The future of deep learning 无监督学习对于重新点燃深度学习的热潮起到了促进的作用。 有监督学习比无监督学习更加成功。

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    Deep Learning综述

    Deep-Learning-Papers-Reading-Roadmap: [1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." (2015) (Three Giants' Survey) Review 机器学习在当下有很多应用:从网络搜索的内容过滤到电商的商品推荐,以及在手持设备相机和智能手机上的应用 Supervised learning 机器学习算法中最常见的形式为监督学习。比如我们想搭建一个图片分类系统,区分马、汽车、人和宠物四类。首先收集这四类的图片,然后打上标签。

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    Towards biologically plausible deep learning

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    Deep Learning的展望

    随着2017年的到来,深度学习技术也迎来了新的一年。深度学习是一门基于多层神经网络的技术,此项技术是许多颠覆性技术(如人工智能、认知计算、实时数据流分析等)的基...

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    Deep Learning Recommendation Model(DLRM)

    概述 DLRM(Deep Learning Recommendation Model)[1]是Facebook在2019年提出的用于处理CTR问题的算法模型,与传统的CTR模型并没有太大的差别,文章本身更注重的是工业界对于深度模型的落地 Deep learning recommendation model for personalization and recommendation systems[J]. arXiv preprint arXiv:1906.00091, 2019. [2] DLRM: An advanced, open source deep learning recommendation model

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    最全深度学习资源集合(Github:Awesome Deep Learning)Awesome Deep Learning

    偶然在github上看到Awesome Deep Learning项目,故分享一下。 以下整理至:Awesome Deep Learning。 2014) Deep Learning by Microsoft Research (2013) Deep Learning Tutorial by LISA lab, University of Montreal Oxford(2014-2015) Deep Learning - Nvidia(2015) Graduate Summer School: Deep Learning, Feature Learning Deep Learning News Machine Learning is Fun!

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    WHEN NOT TO USE DEEP LEARNING

    In this post, I wanted to visit use cases in machine learning where deep learning would not really make Deep learning has become an undeniable force in machine learning and an important tool in the arsenal Deep learning is more than .fit() There is also an aspect of deep learning models that I see gets sort When not to use deep learning So, when does deep learning not fit to the task? The future is deep The deep learning field is hot, well-funded, and moves crazy fast.

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    为Spark Deep Learning 集成

    前两天已经添加了一个 TFTextEstimator:为Spark Deep Learning 添加NLP处理实现,不过只能做hyper parameter tuning,做不了真正的分布式训练,所以正好把这个特性加到了这个 使用方法 建议看这篇文章之前,先看为Spark Deep Learning 添加NLP处理实现。 我给TFTextFileEstimator 添加了一个新的参数叫做 runningMode。

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    Understanding Convolution in Deep Learning(一)

    Why is convolution of images useful in machine learning? 在图像中可能有很多令人分心的信息与我们试图实现的目标不相关。

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    Understanding Convolution in Deep Learning(二)

    我们现在有一个非常好的直觉,卷积是什么,以及卷积网中发生了什么,为什么卷积网络是如此强大。 但我们可以深入了解卷积运算中真正发生的事情。我们将看到计算卷积的原始...

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    Understanding Convolution in Deep Learning(四)

    我们将在下面看到为何卷积内核会被称为过滤器以及卷积操作通常被描述为过滤操作的原因。

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    Yoshua Bengio:The Promise of Deep Learning

    原文如下: The Promise of Deep Learning Humans have long dreamed of creating machines that think. This is the field of deep learning. Deep learning isn’t brand new. We co-authored a paper, Deep Learning, which was published in the journal Nature in May, where we laid I’m co-authoring a book, Deep Learning, with Ian Goodfellow and Aaron Courville. I’m tremendously excited about the future of deep learning.

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    Deep Learning 调参经验

    Batch Normalization据说可以提升效果,不过我没有尝试过,建议作为最后提升模型的手段,参考论文:Accelerating Deep Network Training by Reducing

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    Deep learning调参经验

    Batch Normalization据说可以提升效果,不过我没有尝试过,建议作为最后提升模型的手段,参考论文:Accelerating Deep Network Training by Reducing

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    Understanding Convolution in Deep Learning(五)

    统计模型和机器学习模型有什么区别? 统计模型通常集中在很容易解释的很少的变量。 可以建立统计模型来回答这些问题:药物A是否比药物B好?机器学习模型关于预测性能:...

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    The Brain vs Deep Learning(一)

    ---这是一篇很有深度的文章,把深度学习和大脑做了比较,一步步分析,通俗却不简单。

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    The Brain vs Deep Learning(二)

    --越来越艰难了,会涉及到大量生物学的知识,因为深度学习本来就是借鉴大脑的 Part II: The brain vs. deep learning — a comparativeanalysis 现在我将逐步解释大脑如何处理信息

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    The Brain vs Deep Learning(三)

    生物信息处理的复杂性不是以蛋白质信号传导级联为结束,100亿个蛋白质不是完成其任务的工人的随机汤,而是这些工作者被设计为具有特定数量以服务于与目前相关的特定功能...

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