papering - 博客园

摘要： C:\Python36\python.exe D:/pymine/clean/chained_located/chained_located_dynamic_input.py '-69,-47,,,-72,-40,-37,-96,-36,-97,-67,-67,-43,,-100,-70,-54,-62,-92,-98,,-33,-77,-17,-17,,-98,-76... 阅读全文

posted @ 2017-09-25 18:13 papering 阅读(293) 评论(0) 推荐(0)

What happens when we continue stacking deeper layers on a “plain” convolutional neural network?

摘要： http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture9.pdf The deeper model performs worse, but it’s not caused by overfitting! 阅读全文

posted @ 2017-09-25 16:12 papering 阅读(278) 评论(0) 推荐(0)

A vectorized example

摘要： http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture4.pdf 阅读全文

posted @ 2017-09-25 14:19 papering 阅读(130) 评论(0) 推荐(0)

SVM vs. Softmax

摘要： http://cs231n.github.io/linear-classify/ http://cs231n.github.io/assets/svmvssoftmax.png 阅读全文

posted @ 2017-09-25 11:28 papering 阅读(198) 评论(0) 推荐(0)

2017年9月22日

run length encoding

摘要：直观阅读全文

posted @ 2017-09-22 16:16 papering 阅读(126) 评论(0) 推荐(0)

2017年9月20日

Data Decisions: DSP vs. DMP

摘要： http://www.cmo.com/features/articles/2016/3/9/data-decisions-dsp-vs-dmp.html As marketers assess their requirements for marketing technology, the ques 阅读全文

posted @ 2017-09-20 17:11 papering 阅读(267) 评论(0) 推荐(0)

What I learned from competing against a ConvNet on ImageNet

摘要： http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/ 阅读全文

posted @ 2017-09-20 17:01 papering 阅读(192) 评论(0) 推荐(0)

Image Recognition

摘要： https://www.tensorflow.org/tutorials/image_recognition 阅读全文

posted @ 2017-09-20 16:55 papering 阅读(208) 评论(0) 推荐(0)

They're much closer in spirit to how our brains work than feedforward networks.

摘要： http://neuralnetworksanddeeplearning.com/chap1.html Up to now, we've been discussing neural networks where the output from one layer is used as input 阅读全文

posted @ 2017-09-20 16:26 papering 阅读(239) 评论(0) 推荐(0)

output value . Sigmoid neurons are similar to perceptrons, but modified so that small changes in their weights and bias cause only a small change in their output.

摘要： http://neuralnetworksanddeeplearning.com/chap1.html . Sigmoid neurons are similar to perceptrons, but modified so that small changes in their weights 阅读全文

posted @ 2017-09-20 15:18 papering 阅读(290) 评论(0) 推荐(0)

2017年9月19日

aerospike

摘要： spike 阅读全文

posted @ 2017-09-19 20:33 papering 阅读(179) 评论(0) 推荐(0)

它是个黑盒，无法解释的黑盒；它避免了我们的或者某种来源的先验强加于陌生人：另外一个黑盒。

摘要： @Matthew 【[抱拳]】推荐系统中，传统的两大算法，无论是基于人的过滤，还是基于物品的过滤，在前期历史数据的量和质都不充足的情况下，这两种传统的算法都无法冷启动。【基于ANN的一种解决办法】张三第一次点击了汽车广告，通过ANN训练，使得输出层的汽车的概率最大，次之的就为接下来要投出的广告类阅读全文

posted @ 2017-09-19 20:28 papering 阅读(224) 评论(0) 推荐(0)

bilingual evaluation understudy

摘要： BLEU is designed to approximate human judgement at a corpus level, and performs badly if used to evaluate the quality of individual sentences. https:/ 阅读全文

posted @ 2017-09-19 18:36 papering 阅读(227) 评论(0) 推荐(0)

TensorFlow_action

摘要：安装TensorFlow 包依赖 https://www.tensorflow.org/tutorials/layers Building the CNN MNIST Classifier 【Each of these methods accepts a tensor as input and re 阅读全文

posted @ 2017-09-19 18:10 papering 阅读(507) 评论(0) 推荐(0)

Hadamard Product

摘要： http://mathworld.wolfram.com/HadamardProduct.html 阅读全文

posted @ 2017-09-19 16:21 papering 阅读(231) 评论(0) 推荐(0)

cross-correlation convolution. Hermitian_function

摘要： Hermitian_function 阅读全文

posted @ 2017-09-19 15:13 papering 阅读(129) 评论(0) 推荐(0)

S-形函数广泛应用于ANN 的激活函数

摘要： Logistic function hyperbolic tangent arctangent function Gudermannian function Error function Generalised logistic function Smoothstep function Specif 阅读全文

posted @ 2017-09-19 14:27 papering 阅读(606) 评论(0) 推荐(0)

减少75%的数据量它的精确位置远不及它和其他特征的相对位置的关系重要

摘要： zh.wikipedia.org/wiki/卷积神经网络池化（Pooling）是卷积神经网络中另一个重要的概念，它实际上一种形式的向下采样。有多种不同形式的非线性池化函数，而其中“最大池化（Max pooling）”是最为常见的。它是将输入的图像划分为若干个矩形区域，对每个子区域输出最大值。直觉上阅读全文

posted @ 2017-09-19 14:21 papering 阅读(279) 评论(0) 推荐(0)

The hidden layers are either convolutional, pooling or fully connected.

摘要： https://en.wikipedia.org/wiki/Convolutional_neural_network Convolutional layers apply a convolution operation to the input, passing the result to the 阅读全文

posted @ 2017-09-19 14:15 papering 阅读(229) 评论(0) 推荐(0)

images have the “stationarity” property, which implies that features that are useful in one region are also likely to be useful for other regions.

摘要： Convolutional networks may include local or global pooling layers[clarification needed], which combine the outputs of neuron clusters at one layer int 阅读全文

posted @ 2017-09-19 11:19 papering 阅读(214) 评论(0) 推荐(0)

2017年9月18日

算法报告

摘要：【要解决到的问题】“对于给定的mac-rssi列表，求出特征值。” 阅读全文

posted @ 2017-09-18 21:55 papering 阅读(413) 评论(0) 推荐(0)

word2vec_basic.py

摘要： https://raw.githubusercontent.com/tensorflow/tensorflow/r1.3/tensorflow/examples/tutorials/word2vec/word2vec_basic.py 阅读全文

posted @ 2017-09-18 20:08 papering 阅读(330) 评论(0) 推荐(0)

Softmax_function

摘要： en.wikipedia.org/wiki/Softmax_function 阅读全文

posted @ 2017-09-18 12:04 papering 阅读(153) 评论(0) 推荐(0)

DNN 数据的表示

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posted @ 2017-09-18 04:31 papering 阅读(191) 评论(0) 推荐(0)

Convex combination

摘要： en.wikipedia.org/wiki/Convex_combination 凸组合 In convex geometry, a convex combination is a linear combination of points (which can be vectors, scalars 阅读全文

posted @ 2017-09-18 04:04 papering 阅读(792) 评论(0) 推荐(0)

好为什么好转变思维

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posted @ 2017-09-18 03:41 papering 阅读(167) 评论(0) 推荐(0)

函数的光滑化或正则化卷积应用两个统计独立变量X与Y的和的概率密度函数是X与Y的概率密度函数的卷积

摘要： http://graphics.stanford.edu/courses/cs178/applets/convolution.html Convolution is an operation on two functions f and g, which produces a third funct 阅读全文

posted @ 2017-09-18 02:59 papering 阅读(1173) 评论(0) 推荐(0)

world embedding 嵌入

摘要： http://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization 嵌入 | TensorFlow https://tensorflow.google.cn/programmers_guide 阅读全文

posted @ 2017-09-18 02:01 papering 阅读(245) 评论(0) 推荐(0)

受限于冷启动

摘要： cold start 0-用户 1-物品 2-系统阅读全文

posted @ 2017-09-18 01:30 papering 阅读(133) 评论(0) 推荐(0)

DNN优势

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posted @ 2017-09-18 01:18 papering 阅读(757) 评论(0) 推荐(0)

DNN自适应

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posted @ 2017-09-18 00:44 papering 阅读(270) 评论(0) 推荐(0)

低秩近似 low-rank approximation

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posted @ 2017-09-18 00:35 papering 阅读(1408) 评论(0) 推荐(0)

2017年9月17日

Random Fourier Features

摘要： K-DSN 深度堆叠网络 Random Features for Large-Scale Kernel Machines To accelerate the training of kernel machines, we propose to map the input data to a rand 阅读全文

posted @ 2017-09-17 23:08 papering 阅读(618) 评论(0) 推荐(0)

Convex optimization 凸优化

摘要： zh.wikipedia.org/wiki/凸優化以下问题都是凸优化问题，或可以通过改变变量而转化为凸优化问题：[5] 最小二乘线性规划线性约束的二次规划半正定规划 Convex function Convex minimization is a subfield of optimizati 阅读全文

posted @ 2017-09-17 22:31 papering 阅读(278) 评论(0) 推荐(0)

希尔伯特空间

摘要： zh.wikipedia.org/wiki/希尔伯特空间在数学里，希尔伯特空间即完备的内积空间，也就是说一个带有内积的完备向量空间。是有限维欧几里得空间的一个推广，使之不局限于实数的情形和有限的维数，但又不失完备性（而不像一般的非欧几里得空间那样破坏了完备性）。与欧几里得空间相仿，希尔伯特空间也是阅读全文

posted @ 2017-09-17 21:39 papering 阅读(921) 评论(0) 推荐(0)