rbm auto

In 2006, Hinton's depth belief network (deep RBMs belief, Networks) based on the limited Boltzmann machine (re-stricted Boltzmann machines, DBNs) is the first in the field of deep learning theory in machine learning. One shot, and became the main framework of the deep learning algorithm since then. In this algorithm, DBN is cascaded by several layers of RBM, and thanks to the efficient approximation algorithm of the contrast divergence (contrastive di

RBM (Restricted Boltzman machine, restricted Boltzmann machines) is the basis of deep learning, although the principle is relatively simple, but the actual training used a lot of trick, in the reference, Hinton for us to disclose a few training details.First, the input is a real value vector:When the input V of an RBM is a real-valued vector, the formula for calculating the output H of the hidden layer is c

The RBM uses the energy model.Simply summarize the energy model. Suppose an isolated system (the total energy $e$ must, the number of particles $n$ certain), the temperature is constant 1, each particle has $m$ a possible state, each state corresponds to an energy $e_i$. So, randomly selecting a particle in this system, the probability that the particle is in state $k$, or the proportion of particles with state $k$ is:$ $p (state=k) =\frac{e^{-e_k}}{\

Tags: integrated inf nbsp. com article Network part knowledge randomThe derivation of the Boltzmann machine and the two-value RBM, this part of the program is simple but the theory is not very good to say, involving a lot of random process and probability of knowledge. Bengio that article is actually very detailed, but he is from the point of view of free energy, physical significance is very clear but the derivation process is not as clear as some in

features to discover distributed Feature Representation of data.To overcome problems in neural network training, DL adopts a different training mechanism than neural network. In traditional neural networks, back propagation is used. In short, iterative algorithms are used to train the entire network, and initial values are randomly set to calculate the output of the current network, then, the parameters of the previous layers are changed based on the difference between the current output and th

Links in http://download.csdn.net/detail/lucky_greenegg/5413211 The code is based on the DBN-RBM character recognition of the MATLAB program written in C + + version, HTTP://PAN.BAIDU.COM/S/1MGZIGPQ (There are many people say code comments too little, in order to facilitate understanding can first look at the MATLAB code, relatively short, the data is also converted from the inside, and the MATLAB code can be seen directly after the image results, C

represent the symbols of each part of the equation, but rather indicate the effect on the probability density in the model. The first part increases the probability of training data (by reducing the corresponding free energy), and the second part of reducing the model to determine the descent gradient is often difficult because he involves computation. This is nothing more than the expectation in all configurations (conforming to the probability distribution generated by the model)! The first

Original example, Class [Cpp] // auto-increment and auto-increment operations, prefix suffixes, and suffixes call prefixes! = Call =# Include "head. h"// Used to process Arrays// Start with exercise 14_23 and complete the class. Go to the back of the Code.Class CheckedPtr {Public:CheckedPtr (int * B, int * e): beg (B), end (e), curr (B ){}Public:CheckedPtr operator ++ ();CheckedPtr operator --();// Add a

The grid template (Grid-template) attribute and its general notation (longhands) define a fixed number of tracks that form an explicit mesh.When grid items are positioned outside these boundaries, the grid container generates an implicit grid track by increasing the implicit grid lines.These implicit and explicit grid lines together form an implicit grid (implicit grid).The dimensions of an implicit mesh track are determined by the grid automatic row (grid-a