machine learning and neural networks

want to go down the hill, how can you go down the hill as soon as possible (by default, the speed is constant and you will not die )? You should look around and find the steep current direction to go down the hill? In this direction, the gradient can be used for calculation, which is the source of the gradient descent method. Do you think it is very simple, think you have mastered it? Haha, it's still too young. I will not go into details about this part. I will provide two materials for my stu

Feedforward network, for example, we look at the typical two-layer network of Figure 5.1, and examine a hidden-layer element, if we take the symbol of its input parameter all inverse, take the tanh function as an example, we will get the opposite excitation function value, namely Tanh (−a) =−tanh (a). And then the unit all the output connection weights are reversed, we can get the same output, that is to say, there are two different sets of weights can be obtained the same output value. If ther

is going when it is initialized, or we don't know where the driving direction is, only after the learning algorithm has been running long enough that the white section appears in the entire gray area, showing a specific direction of travel. This means that the neural network algorithm at this time has chosen a clear direction of travel, not like the beginning of the output of a faint light gray area, but t

Motive (motivation)For non-linear classification problems, if multiple linear regression is used to classify, it is necessary to construct many high-order items, which leads to too many learning parameters, so the complexity is too high.Neural networks (Neural network)As shown in a simple neural network, each circle re

the above accuracy problems:But the calculation is almost twice times the amount of (5.68). In fact, the calculation of numerical methods can not take advantage of the previous useful information, each derivative needs to be calculated independently, the calculation can not be simplified.But the interesting thing is that the numerical derivative is useful in another place--gradient check! We can use the results of the central differences and the derivative of the BP algorithm to compare, in ord

). In fact, the calculation of numerical methods can not take advantage of the previous useful information, each derivative needs to be calculated independently, the calculation can not be simplified.But the interesting thing is that the numerical derivative is useful in another place--gradient check! We can use the results of the central differences and the derivative of the BP algorithm to compare, in order to determine whether the BP algorithm execution is correct.Starting today to learn the

ifSelf. Steps dm:self. Steps= 5*DM#set the minimum number of iterations forIinchxrange (self. Steps): Lrate,r= Self.ratecalc (i)#1) Calculate the learning rate and the classification radius under the current iteration countself.lratelist.append (lrate) self.rlist.append (R)#2) Randomly generate a sample index and extract a sampleK =random.randint (0,DM) mysample=normdataset[k,:]#3) Calculate the optimal node: Returns the index value of

Deep Learning SpecializationWunda recently launched a series of courses on deep learning in Coursera with Deeplearning.ai, which is more practical compared to the previous machine learning course. The operating language also has MATLAB changed to Python to be more fit to the current trend. A study note on this series o

The third lecture of Professor Geoffrey Hinton's Neuron Networks for machine learning mainly introduces linear/logical neural networks and backpropagation, and the following is a tidy note.Learning the weights of a linear neuronThis section introduces the

=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;%-------------------------------------------------------------% ====

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 BackProp

**************************************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

; -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 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. S

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

time the entire training set runs out, the neuron gets 4 times times The input of the ownership value.Without any distinction, there is no way to differentiate between the two (non-circular patterns can be identified).Using hidden neuronsLinear neurons are also linear and do not increase the ability to learn in the network.The nonlinearity of the fixed output is not enough.The weights of learning hidden layers are equivalent to the

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

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

noise in the activities as a regularizer). Presumably, for an implicit unit that uses a logical function, its output must be between 0 and 1, and now we use a binary function in the forward direction instead of the logic function in the hidden unit, the random output 0 or 1, the output is computed. Then in the reverse, we use the correct method to do the correction. The resulting model may have a poor performance on the training set, and the training speed is slower, but its performance on the