best gpu for neural networks

convolution layer of the error-sensitive items, because the reverse propagation when the output is smaller than the input, so the gradient at the time of transmission and traditional BP algorithm, So how to get the error-sensitive item of convolutional layer is the problem to consider. The third problem is to consider the pooling layer below the convolution layer, this is because we want to get the pooling layer error sensitivity, relying on the convolution core error sensitive, also because of

nonlinearity of the network, but also maintain the sensation field of the previous layer, so it has a good effect on the detection of small objects. The original 5x5 convolution kernel is replaced by two 3x3 convolution cores, reducing the parameters, increasing the nonlinearity of the network and the module sensing field. Hypernet:concatenation of Multi-scale Intermediate outputs Hypernet the convolution level of different convolution stages, it has a good effect on the detection

1. Introduction:YouTube's recommended challenges:Scale: Many algorithms are useful in small data, which is useless on YouTube;Freshness: Need to be sensitive to the new uploaded video;Noisy: no real user feedback; lack of structured data2. Skip3. Candidate Generation:The previous model was based on matrix decomposition; The first layers of the YouTube DL model are the use of neural networks to simulate this

After going through a lot of resumes, and decided to continue to recharge their otl, and began to learn the neural network this piece.Found the classic textbook of deep learning. Online Address: http://neuralnetworksanddeeplearning.comBut here is python2.7, and I learned is python3, so some code can not directly exactly shown, first put on Python3 and python2 what is different.Then record what needs to change in the course of learning:Chapter One (ide

on the TOPN item to do embedding, the rest of the direct embedding is 0. The multivalent feature, like "past clicks", is the same as the recall phase, with a weighted average. Another notable thing is that embedding with the same ID as the same dimension feature is shared (such as "Past video id", "Seed video id"), which can greatly speed up training, but obviously the input layer is still populated separately. (This sentence is not very understanding)NN is sensitive to scale, and a normalized

Learning Goals: Understand the challenges of object Localization, Object Detection and Landmark finding Understand and implement Non-max suppression Understand and implement intersection over union Understand how we label a dataset for an object detection application Remember the vocabulary of object Detection (landmark, anchor, bounding box, grid, ...) "Chinese Translation"Learning Goals: Understand The challenges of object positioning, target detection, and

In both CNN (1) and CNN (2) Two articles, the main explanation is CNN's basic architecture and weight sharing (Weight sharing), this article focuses on the convolution part.First, before convolution, our data is 4D tensor (width,height,channels,batch), which was mentioned in CNN (1): Architecture. The passage here, and the previously mentioned depth, is a concept, such as a grey scale image with a channel number of 1;RGB graphs of 3.In fact, Kernel also has channel, and its number is the same as

Learn this Blog directory full connection God will Network's echo propagation algorithmForward propagation of backward propagation algorithm for forward propagation backward propagating fully connected neural networks Reference connection List the formulas in the paper and correspond to the process one by one shown in the figure above: Cost function:EN=12∑N=1N∑K=1C (tnk−ynk) 2 e^n = \frac{1}{2}\sum_{n=