coursera neural networks

== output Dimension Model.add (Activation (' Relu ')) # Activation function Model.add (dropout (0.5)) #dropout#第二层model. Add (Dense (nb_classes)) Model.add (Activation (' Softmax '))#损失函数设置, optimization function, metric model.compile (loss= ' categorical_crossentropy ', optimizer= ' Adam ', metrics=[' Accu Racy '])#训练, cross-validation history = Model.fit (X_train, Y_train, Nb_epoch=nb_epoch, Batch_size=batch_size, Verbose=1, validation_split=0.1) score = Model.evaluate (X_test, Y_test, Batch_s

weight update, is by a lot of weight multiplied, the smaller, a bit like the gradient disappears meaning (this sentence is I added) 8: If training rnn or LSTM, It is important to ensure that the norm of the gradient is constrained to 15 or 5 (provided that the gradient is first normalized), which is significant in RNN and lstm. 9: Check the gradient below, if it is your own calculation. 10: If you use LSTM to solve the problem of long-time dependencies, remember to initialize bias 12: As far as

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=

modulation gate, memory cell and output gate.Each of the LSTM layers have hidden states.3. Loss function and optimizationThe conditional probability of the poses Yt = (y1, ..., YT) given a sequence of monocular RGB images Xt = (x1, ..., XT) up to time t.Optimal Parameters:The hyperparameters of the Dnns:(pk,φk) is the ground truth pose.(p?k,φ?k) is the estimated ground truth pose.κ (the experiments) is a scale factor to balance the weights of positions and orientations.N is the number of sample

alexnet Summary Notes Thesis: "Imagenet classification with Deep convolutional neural" 1 Network Structure The network uses the logic regression objective function to obtain the parameter optimization, this network structure as shown in Figure 1, a total of 8 layer network: 5 layer of convolution layer, 3 layer full connection layer, and the front is the image input layer. 1) convolution layer A total of 5-layer convolution layer, known from the struc

);Update.mfunction [W] = update (w,q,x,t,a)% update the values in W[m,n] = size (w); Nq =; % distance of neighborfor j = q-nq:q+nqif J Test.mfunction [Res] = Test (im,w)% test for the image data compression based on SOM network% (256*256)/(4*4)% will (256*256) be divided (4*4) ) image block n = 4;m = 4;block_n = N*ones (1,256/n); % Block_n = [4,4....4] 64 4block_m = M*ones (1,256/m); im_block = Mat2cell (im,block_n,block_m);%im_block = Reshape (im_bloc k,1,4096); X = ones (16

The Mcculloch-pitts model for neuronsNeuron: The basic Information Processing Unit for neural network operations.The basic elements of neurons: synapses, adders, biases, activation functions.Neuron Mathematical expression:Name of the UK: output of the linear assemblyVK=UK+BK: Induction of local domain, activation potential.The role of bias is to do affine transformations for the UK.Type of activation function: threshold function, sigmoid function.Intr

training:Eventually:Look at the weights for each unit, sort of like a number template.Why the simple learning algorithm is insufficienta The layer network with a winner in the top layer are equivalent to have a rigid template for each shape., Haven Winner is the template, which has the biggest overlap with the ink.the ways in which hand-written digits vary is much too complicated to being captured by simple template matches of whole s Hapes.–to capture all the allowable variations of a digit we

than Max equals Max.Because it is time-consuming to generate random numbers from a computer, it is generally implemented in the first way, due to the acceleration of consideration.But the inverse of the first method function is 0, and the gradient can not be propagated in reverse. In addition, the gradient has a cumulative effect, that is, the gradient with a certain amount of noise, and noise is generally considered to obey the normal distribution, so, multiple cumulative gradient to the avera

First, CNN's Principle 1, CNN thought:(1) Using Hopfield neural network and CAA, nonlinear dynamics of Hopfield (mainly for optimization problems, such as NP problems such as travel quotient), the concept of Hopfield energy function, Hopfield solves the problem of analog circuit implementation.b, CA cell automata, local connection time and space are discrete dynamics system, CNN borrowed from CA's cell concept and locality, consistency, parallelism an

At present, the rise of artificial intelligence is mainly based on the development of deep learning, but this method does not allow the computer to learn a small number of samples like humans can generalize knowledge into many kinds of problems, which also means that the system application scope is limited. Recently, vicarious, a well-known AI startup company, published a new probabilistic generation model in science. The new model has the ability of recognition, segmentation and reasoning, and

set, the KL distance is the indicator that describes the diversity, thus reducing the amount of computation. Traditional deep learning will need to do before the training of data enhancement, each sample is equal; This article contains some data enhancement not only does not play a good role, but brings the noise, it needs to do some processing, but also some of the data does not need to be enhanced, which reduces noise and saves calculation. Qa Q: Why did the active learning not b

while achieving the accuracy of the complex and compact depth model".Summarize: The author requests that "Block" has the same topological structure, and gives the design principle and template of "blcok" extension (through repeating building blocks can draw the network structure), which greatly simplifies the work of network structure design. The same implementation of different equivalent forms of the given, one can deepen our understanding, the second can provide us with the poss