best gpu for neural networks

Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." Nature 529.7587 (2016): 484-489.Alphago's thesis, the main use of the RL technology, do not know before the use of RL to do Weiqi.Proposed two networks, one is the strategy network, one is the value network, all through the self-battle realization.Policy Network:The strate

I. Documentation names and authorsconvolutional neural Networks at Constrained time COST,CVPR two. Reading timeJune 30, 2015Three. Purpose of the documentThe author hopes to improve the accuracy of CNN by modifying the model depth and the parameters of the convolution template, while maintaining the computational complexity. Through a lot of experiments, the author finds the importance of different paramete

ImageNet classification with deep convolutional neural Networks reading notes(after deciding to read a paper each time, the notes are recorded on the blog.) ）This article, published in NIPS2012, was Hinton and his students, in response to doubts about deep learning, used deep learning for imagenet, the largest database of image recognition, and eventually achieved very surprising results, The result is much

is: F ' (x) = f (x) * (1-f (x)), calculation is very convenient. The code is as follows:1%%compute gradients using backpropagation2 3%%% YOUR CODE here%%%4%Output Layer5Output =Zeros (Size (Pred_prob));6Output (Index) = 1;7Error = Pred_prob-output;8 9 forL = numhidden+1: -1:1Tengradstack{l}.b = SUM (error,2); One if(L = = 1) AGradstack{l}. W = Error * Data'; - Break; - Else theGradstack{l}. W = error * HACT{L-1}'; - End -Error = (Stack{l}. W'*error. * HACT{L-1}. * (1-hact{l-1}

,x_test) Y_prediction_train = Predict (W,b,x_train) # # # # END CODE here # # # print train/test Errors print ("Train accuracy: {}% ". Format (100-np.mean (Np.abs (y_prediction_train-y_train) *)) print (" Test accuracy: {}% ". Format (100-np.me An (Np.abs (y_prediction_test-y_test)) *) d = {"Costs": costs, "y_prediction_test": Y_prediction_test, "Y_prediction_train": Y_prediction_train, "W": W, "B": B, "learning_rate": Learnin G_rate, "Num_iteRations ": num_iterations} print (d[" costs

\):The chain rules are updated as follows:\[\begin{split}\frac{c_0}{\partial \omega_{jk}^{(L)}}= \frac{\partial z_j^{(L)}}{\partial \omega_{jk}^{(l)}}\ Frac{\partial a_j^{(L)}}{\partial z_j^{(l)}}\frac{\partial c_0}{\partial a_j^{(L)}}\=a^{l-1}_k \sigma\prime (z^ {(l)}_j) 2 (a^{(l)}_j-y_j) \end{split}\]And to push this formula to other layers ( \frac{c}{\partial \omega_{jk}^{(L)}}\) , only the \ (\frac{\partial c}{\partial a_j^{) in the formula is required ( L)}}\) .Summarized as follows:Therefo

1 Introduction In this article, we will introduce a framework aforge using C #, which allows you to easily manipulate artificial networks, computer vision, machine learning, image processing, genetic algorithms, etc. Introduction of 2 neural network design part framework Here, I want to emphasize: This piece of code is very beautiful, a code such as poetic beauty, let me charmed. This piece of code is i

http://blog.csdn.net/pipisorry/article/details/4397356Machine learning machines Learning-andrew NG Courses Study notesNeural Networks Representation Neural network representationnon-linear Hypotheses Nonlinear hypothesisNeurons and the brain neurons and brainsModel representation models representExamples and intuitions examples and intuitive knowledgeMulticlass Classification Multi-class classificationfrom:

self.nodesinLayers.append (int (SELF.OUTPUTDI)) #self. nodesinb=[] #self. nodesinb + = self. Nodesinhidden #self. Nodesinb.append (int (SELF.OUTPUTDI)) #for element in Self.nodesinlayers: #self . Nodesinlayers=int (Self.nodesinlayers[idx]) #weight matrix, it's a list and each element is a numpy matrix # Weight matrix, here are Wij, and in BP we could inverse it into Wji #here we store the matrix as Numpy.array SE Lf.weightmatrix=[] Self. B=[] for IDX in range (0,self.NL-1): #Xaxier ' s scaling

);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

; -j = j + lambda* (sum(sum(Theta1 (:,2: End). ^2))+sum(sum(Theta2 (:,2: End). ^2)))/2/m; + -%Backward Propagation +Delta1 = zeros (Size (Theta1)); %25x401 ADelta2 = zeros (Size (THETA2)); %0x26 at forI=1: M -DELTA3 = A3 (i,:)'-Y_vect (i,:)'; %0x1 -TEMPTHETA2 = Theta2'* DELTA3;% 26x10x10x1 = 26x1 -Delta2 = TempTheta2 (2: End). * Sigmoidgradient (Z2 (i,:)'); %25x1 -Delta2 = Delta2 + delta3 * A2 (i,:); %10x1x1x26 -Delta1 = Delta1 + delta2 * A1 (I,:); %25x1x1x401 in end; - toTheta2_grad = delt

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