neural networks and learning machines 3rd edition

=sigmoid (Z2); A2=[ones (1,size (a2,2)); A2]; Z3=THETA2*A2; A3=sigmoid (Z3); Delta_3=a3-y_vec; Gz2=[0;sigmoidgradient (z2)]; Delta_2=theta2 ' *delta_3.*gz2; Delta_2=delta_2 (2:end); Delta2=delta2+delta_3*a2 '; Delta1=delta1+delta_2*a1 '; endtheta1_grad=1/m*delta1; THETA2_GRAD=1/M*DELTA2; Theta1 (:, 1) = 0; Theta1_grad=theta1_grad+lambda/m*theta1; THETA2 (:, 1) = 0; theta2_grad=theta2_grad+lambda/m*theta2;%-------------------------------------------------------------% ====

relationship between word, extracted a lot of features. Which extracts the sentence features used by CNN. Convolution, Pooling,softmax, just a few processes. Le, Quoc v., and Tomas Mikolov."distributed representations of sentences and Documents." ICML (2014). Extension of the Word2vec model.In fact, we all feel that the deep model is able to extract images and other signals of the latent variables, then it should be very natural to extract the text topic out, LDA and so on is noth

Original: https://medium.com/learning-new-stuff/how-to-learn-neural-networks-758b78f2736e#.ly5wpz44dThe second post in a series of me trying to learn something new over a short period of time. The first time consisted of learning how to does machine learning in a week.This t

used in the Googlenet V2.4, Inception V4 structure, it combines the residual neural network resnet.Reference Link: http://blog.csdn.net/stdcoutzyx/article/details/51052847Http://blog.csdn.net/shuzfan/article/details/50738394#googlenet-inception-v2Seven, residual neural network--resnet(i) overviewThe depth of the deep learning Network has a great impact on the fi

function changes when weights and deviations are changed. Although this expression is complex, it has its own mathematical beauty, with each element having a natural visual interpretation. So the reverse propagation is not just a fast algorithm for learning, it actually gives us an insight into the changes in the behavior of the network as it changes weights and biases. This is where we understand the meaning of the reverse propagation algorithm in d

value sharing (or weight reproduction) and time or spatial sub-sampling to obtain some degree of displacement, scale and deformation invariance.Question three:If the C1 layer is reduced to 4 feature plots, the same S2 is also reduced to 4 feature plots, with C3 and S4 corresponding to 11 feature graphs, then C3 and S2 connection conditionsQuestion Fourth:Full connection:C5 to the C4 layer convolution operation, the use of the full connection, that is, each C5 convolution core in S4 all 16 featu

Bengio, LeCun, Jordan, Hinton, Schmidhuber, Ng, de Freitas and OpenAI had done Reddit AMA's. These is nice places-to-start to get a zeitgeist of the field.Hinton and Ng lectures at Coursera, UFLDL, cs224d and cs231n at Stanford, the deep learning course at udacity, and the sum Mer School at IPAM has excellent tutorials, video lectures and programming exercises that should help you get STARTED.NB Sp The online book by Nielsen, notes for cs231n, and blo

This series of articles is the study notes of "machine learning", by Prof Andrew Ng, Stanford University. This article is the notes of week 5, neural Networks learning. This article contains some topic on cost Function and backpropagation algorithm.Cost Function and BackPropagationNeural

**************************************Note: This blog series is for bloggers to learn the "machine learning" course notes from Professor Andrew Ng of Stanford University. Bloggers deeply learned the course, do not summarize is easy to forget, according to the course plus their own to do not understand the problem of the addition of this series of blogs. This blog series includes linear regression, logistic regression,

The fourth lecture of Professor Geoffery Hinton's Neuron Networks for machine learning mainly describes how to use the back propagation algorithm to learn the characteristic representation of a vocabulary.Learning to predict the next wordThe next few sections focus on how to use the back propagation algorithm to learn the feature representation of a vocabulary. Starting with a very simple example, we introd

theoretical knowledge : Deep learning: 41 (Dropout simple understanding), in-depth learning (22) dropout shallow understanding and implementation, "improving neural networks by preventing Co-adaptation of feature detectors "Feel there is nothing to say, should be said in the citation of the two blog has been made very

; -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

Welcome reprint, Reprint Please specify: This article from Bin column Blog.csdn.net/xbinworld.Technical Exchange QQ Group: 433250724, Welcome to the algorithm, technology interested students to join.Recently, the next few posts will go back to the discussion of neural network structure, before I in "deep learning Method (V): convolutional Neural network CNN Class

fast.–we already know a lot about themThe MNIST database of hand-written digits is the and the machine learning equivalent of fruit flies–they is publicly available and we can get machine learning algorithm to learn what to recognize these handwritten digits, so it's easy to try lots of variations. them quite fast in a moderate-sized neural net.–we know a huge a

= Mnist.train.next_batch (batch_size)Sess.run (Optimizer, feed_dict={x:batch_x, y:batch_y, keep_prob:dropout})If step% Display_step = = 0:# Calculate loss value and accuracy, outputLoss, acc = Sess.run ([cost, accuracy], feed_dict={x:batch_x, Y:batch_y, Keep_prob:1.})Print "Iter" + str (step*batch_size) + ", Minibatch loss=" + "{:. 6f}". Format (Loss) + ", Training accuracy=" + "{:. 5f}". f Ormat (ACC)Step + = 1Print "Optimization finished!"# Calculate Test AccuracyPrint "Testing accuracy:", se

better than the Model 1. In the lower right table, the training time of Model 1 and Model 2 is 40hours and 30hours respectively, while the error ratio of the two is 25:15, which shows that the time of training of Model 2 is shorter and less than that of model 1.Convolutional nets for object recognitionIn this section we use convolutional neural networks to achieve object recognition. The handwritten number

/ahr0cdovl2jsb2cuy3nkbi5uzxqv/font/5a6l5l2t/fontsize/400/fill/i0jbqkfcma==/dissolve/70/gravity /center "Width=" >Circular simple Pattern recognitionWatermark/2/text/ahr0cdovl2jsb2cuy3nkbi5uzxqv/font/5a6l5l2t/fontsize/400/fill/i0jbqkfcma==/dissolve/70/gravity /center "Width=" >Regardless of mode A or pattern B, each time the entire training set runs out, the neuron gets 4 times times The total weight of the input.No matter what the difference. There is no way to differentiate between the two (non

to stop training.Limiting the size of the weightsThis section describes how to control the capacity of a network by limiting the size of the weights, and the standard method is to introduce a penalty to prevent the weights from becoming too large. Along with some implicit assumptions, neural networks with small weights are much simpler than power values. We can use several different methods to limit the we

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 represent

Neural Networks are getting angry again. Because deep learning is getting angry, we must add a traditional neural network introduction, especially the back propagation algorithm. It is very simple, so it is not complicated to say anything about it. The neural network model i