Must Know Tips/Tricks in Deep Neural Networks (by Xiu-Shen Wei)   Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with…

http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with mul…

转自: http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html…

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…

http://handong1587.github.io/deep_learning/2015/10/09/training-dnn.html  //转载于 Training Deep Neural Networks  Published: 09 Oct 2015  Category: deep_learning Tutorials Popular Training Approaches of DNNs — A Quick Overview https://medium.com/@asjad/p…

On Explainability of Deep Neural Networks « Learning F# Functional Data Structures and Algorithms is Out!   On Explainability of Deep Neural Networks During a discussion yesterday with software architect extraordinaire David Lazar regarding how every…

Introduction to Deep Neural Networks Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw…

Deep Neural Networks are the more computationally powerful cousins to regular neural networks. Learn exactly what DNNs are and why they are the hottest topic in machine learning research. The term deep neural network can have several meanings, but on…

Classifying plankton with deep neural networks The National Data Science Bowl, a data science competition where the goal was to classify images of plankton, has just ended. I participated with six other members of my research lab, the Reservoir lab o…

(Deep) Neural Networks (Deep Learning) , NLP and Text Mining 最近翻了一下关于Deep Learning 或者 普通的Neural Network在NLP以及Text Mining方面应用的文章,包括Word2Vec等,然后将key idea提取出来罗列在了一起,有兴趣的可以下载看看: http://pan.baidu.com/s/1sjNQEfz 我没有把一些我自己的想法放到里面,大家各抒己见,多多交流. 下面简单概括一些其中的几篇p…

声明:所有内容来自coursera,作为个人学习笔记记录在这里. Initialization Welcome to the first assignment of "Improving Deep Neural Networks". Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help…

1. 深层神经网络(Deep L-layer neural network ) 2. 前向传播和反向传播(Forward and backward propagation) 3. 总结 4. 深层网络中的前向传播(Forward propagation in a Deep Network) 向量化实现过程可以写成: 注:这里只能用一个显示for循环,l 从 1 到 L,然后一层接着一层去计算. 如何减少bug 4.1 核对矩阵的维数(Getting your matrix dimensions…

Scalable Object Detection using Deep Neural Networks 作者: Dumitru Erhan, Christian Szegedy, Alexander Toshev, and Dragomir Anguelov 引用: Erhan, Dumitru, et al. "Scalable object detection using deep neural networks." Proceedings of the IEEE Confere…

Understanding, generalisation, and transfer learning in deep neural networks FEBRUARY 27, 2017   This is the first in a series of posts looking at the ‘top 100 awesome deep learning papers.’ Deviating from the normal one-paper-per-day format, I’ll ta…

Deep Neural Network - Application Congratulations! Welcome to the fourth programming exercise of the deep learning specialization. You will now use everything you have learned to build a deep neural network that classifies cat vs. non-cat images. In…

Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision. 学习目标 See deep neural networks as successive blocks put one after each other Build and train a deep L-layer Ne…

Training (deep) Neural Networks Part: 1 Nowadays training deep learning models have become extremely easy with high-quality libraries such as Torch and Theano. These libraries are really helpful for rapidly prototyping deep learning models even witho…

Mastering the game of Go with deep neural networks and tree search Nature 2015  这是本人论文笔记系列第二篇 Nature 的文章了,第一篇是 DQN.好紧张!好兴奋! 本文可谓是在世界上赚够了吸引力! 围棋游戏被看做是 AI 领域最有挑战的经典游戏,由于其无穷的搜索空间 和 评价位置和移动的困难.本文提出了一种新的方法给计算机来玩围棋游戏,即:利用 "value network" 来评价广泛的位置 和 “p…

Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." Nature 529.7587 (2016): 484-489. 推荐PPT:https://wenku.baidu.com/view/3cbb606f49649b6648d747fb.html?from=search Alphago的论文,主要使用了RL的技术,不知道之前有没有用RL做围棋的.RL之外的…

Why are Eight Bits Enough for Deep Neural Networks? Deep learning is a very weird technology. It evolved over decades on a very different track than the mainstream of AI, kept alive by the efforts of a handful of believers. When I started using it a…

本文主要实验文献文献<Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding>算法,在tiny-yolo coco上的压缩效果,在darknet基础上,编写该算法进行压缩实验,结果如下: 原始模型大小64M:mAP=0.224 训练500次,模型大小54M:mAP=0.203 训练5000次,模型大小49M:mAP=0.214 训练50000…

Imagine you're an engineer who has been asked to design a computer from scratch. One day you're working away in your office, designing logical circuits, setting out AND gates, OR gates, and so on, when your boss walks in with bad news. The customer h…

The unstable gradient problem: The fundamental problem here isn't so much the vanishing gradient problem or the exploding gradient problem. It's that the gradient in early layers is the product of terms from all the later layers. When there are many…

文章:Clustering Convolutional Kernels to Compress Deep Neural Networks 链接:http://openaccess.thecvf.com/content_ECCV_2018/papers/Sanghyun_Son_Clustering_Kernels_for_ECCV_2018_paper.pdf 这篇文章主要是研究模型的压缩和加速.其他的文章大多数都只研究网络结构中的冗余参数或影响不大的结构,用剪枝的方法来压缩模型.作者从另一个方…

第四周:深层神经网络(Deep Neural Networks) 深层神经网络(Deep L-layer neural network) 目前为止我们学习了只有一个单独隐藏层的神经网络的正向传播和反向传播,还有逻辑回归,并且你还学到了向量化,这在随机初始化权重时是很重要.本周所要做的是把这些理念集合起来,就可以执行你自己的深度神经网络. 严格上来说逻辑回归也是一个一层的神经网络,浅与深仅仅是指一种程度.有一个隐藏层的神经网络,就是一个两层神经网络.当我们算神经网络的层数时,我们不算输入层,我们只…

译自:http://sebastianruder.com/multi-task/ 1. 前言 在机器学习中,我们通常关心优化某一特定指标,不管这个指标是一个标准值,还是企业KPI.为了达到这个目标,我们训练单一模型或多个模型集合来完成指定得任务.然后,我们通过精细调参,来改进模型直至性能不再提升.尽管这样做可以针对一个任务得到一个可接受得性能,但是我们可能忽略了一些信息,这些信息有助于在我们关心的指标上做得更好.具体来说,这些信息就是相关任务的监督数据.通过在相关任务间共享表示信息,我们的模型在…

大师Geoff Hinton关于Deep Neural Networks的建议 Note: This covers suggestions from Geoff Hinton's talk given at UBC which was recorded May 30, 2013. It does not cover bleeding edge techniques. 主要分为如下几点展开: Have a Deep Network. 1-2个hidden layers被认为是一个shallow n…

论文:Deep Neural Networks for YouTube Recommendations 发表时间:2016 发表作者:(Google)Paul Covington, Jay Adams, Emre Sargin 发表刊物/会议:RecSys 论文链接:论文链接 这篇论文是google的YouTube团队在推荐系统上DNN方面的尝试,发表在16年9 月的RecSys会议.本文就focus在YouTube视频推荐的DNN算法,文中不但详细介绍了Youtube推荐算法和架构细节,还给了…

论文笔记-IGCV3:Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks 2018年07月11日 14:05:46 Liven_Zhu 阅读数 846   介绍 在这篇论文中,作者同时使用低秩核和稀疏核(low-rank and sparse kernel)来组成一个密集kernel.基于ICGV2的基础上,作者提出了ICGV3. 近几年,卷积网络在计算机视觉上的有效性已经得到了验证.目前卷积网络的…

Lesson 2 Improving Deep Neural Networks:Hyperparameter tuning, Regularization and Optimization 这篇文章其实是 Coursera 上吴恩达老师的深度学习专业课程的第二门课程的课程笔记. 参考了其他人的笔记继续归纳的. 训练,验证,测试集 (Train / Dev / Test sets) 在机器学习发展的小数据量时代,常见做法是将所有数据三七分,就是人们常说的 70% 训练集,30% 测试集.如果明确设…