hinton neural networks

bias is used to measure the ability of an independent variable in a multivariate function to influence the function value. A gradient is a vector that points to the value of the function to increase the fastest direction. The chain rule is that, for a composite function, the derivation process can be part of a part, and then "linked" up. Vectors can be thought of as a special form of a matrix. Matrix multiplication is closely related to linear systems. The Ndarray in the Num

"Convolutional neural Networks-evolutionary history" from Lenet to Alexnet This blog is "convolutional neural network-evolutionary history" of the first part of "from Lenet to Alexnet" If you want to reprint, please attach this article link: http://blog.csdn.net/cyh_24/article/details/51440344 More related blog please poke: http://blog.csdn.net/c

Recaption on CNN ArchitectureAlthough Serena is very beautiful, and Justin is a better lecturer. Love him.Recurrent neural Network Meant to process sequencial data, reuse hidden state to retain the knowledge of the previous Fed inputs. Can is use with "one to many", "many to one" and "many to many" scenarios by using different input and output stradegies. Formally, we maintain an $h _t$ for TTH iteration, and generate next hidden state by applying $h

object always correspond to the same block of standard pixels of the image. In addition, the box can provide invariance for many different degrees of freedom: translation, rotation, scale, shear, stretch, and so on. However, it is very difficult to choose a box, because there may be some problems such as segmentation error, covering, singular angle of view and so on.The method of brute force generalization (the Brute forces normalization approach) is given.The third and fourth methods are descr

Refer to:The unreasonable effectiveness of recurrent neural NetworksRecurrent neural Networks sequences . Depending on your background you might being wondering: What makes recurrent Networks so special ? A glaring limitation of Vanilla neural

);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

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

The pooled layers (Pooling layer) are also inspired by visual neuroscience. In the primary visual cortex V1 (Primary visual cortex), there are many complex cells (Complex cells) that are invariant to small changes in objects in the image (invariance to small shifts and Distortions). This invariance is also the core of pooling layer, we first see how the pooling layer works, and then specifically analyze this invariance.We illustrate the working process of the pooling layer, in the max pooling op

This article mainly introduces the knowledge of Perceptron, uses the theory + code practice Way, and carries out the learning of perceptual device. This paper first introduces the Perceptron model, then introduces the Perceptron learning rules (Perceptron learning algorithm), finally through the Python code to achieve a single layer perceptron, so that readers a more intuitive understanding. 1. Single-layer Perceptron model Single-layer perceptron is a neura