convolutional neural network tutorial

BP (backward propogation) neural networkSimple to understand, neural network is a high-end fitting technology. There are a lot of tutorials, but in fact, I think it is enough to look at Stanford's relevant learning materials, and there are better translations at home: Introduction to Artificial neural

network);5. Rnns is implemented based on Python and Theano, including some common Rnns models. Unlike traditional Fnns (Feed-forward neural Networks, forward feedback neural networks), Rnns introduces a directional loop that can handle the problems associated with those inputs. The directional loop structure is shown in the following figure: The

efficiency. The number of neurons that are linearly increased can be expressed in a number of different concepts that increase exponentially.Another advantage of distributed characterization is that the expression of information is not fundamentally compromised, even in the event of a local hardware failure.This idea let Geoffrey Hinton Epiphany, so that he has been in the field of neural network research

)}} {\partial h^{(t)}} \frac{\partial h^{(t)}}{\partial U} = \sum\limits_{t=1}^{\tau}diag (n (h^{(t)}) ^2) \delta^{(t)} (x^{ (t)}) ^t$$In addition to the gradient expression, RNN's inverse propagation algorithm and DNN are not very different, so here is no longer repeated summary.5. RNN SummaryThe general RNN model and forward backward propagation algorithm are summarized. Of course, some of the RNN models will be somewhat different, the natural forward-to-back propagation of the formula will be

This paper is reproduced from http://blog.csdn.net/ironyoung/article/details/49455343 BP (backward propogation) neural networkSimple to understand, neural network is a high-end fitting technology. There are a lot of tutorials, but in fact, I think it is enough to look at Stanford's relevant learning materials, and there are better translations at home: Introdu

information transfer rates (network throughput) Low-cost, small-scale construction of a particular structure network How to add a priori information to a neural network: There is no effective rule to achieve A special process can be implemented: Restricting th

) # padding for I in range (self.size): Self.a[i] = Np.zeros (Self.n[i]) # full 0 Self.z[i] = Np.zeros (Self.n[i]) # full 0 Self.data_a[i] = Np.zeros (Self.n[i]) # Full 0 if I The complete code below is what I have learned from the Stanford Machine Learning tutorial, completely self-tapping: Import NumPy as NP "Reference: Http://ufldl.stanford.edu/wiki/index.php/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C" class Neuralnetworks (object): "" Def __init__ (s

/1406.2661.gan first Paper:lan Goodfellow generative adversarial Networks 5. Algorithm: Using random gradient descent method to train d,g. Specifically also in the above article. 6.DCGAN Principle Introduction: The best model for image processing applications in deep learning is CNN, how CNN and Gan combine. The answer is Dcgan. The principle is the same as Gan. Just replaced the above G and D with two convolutional

the face have moved to another corner of the image, as shown in Fig. 3:The same number of activations occurs in this example, however they occur in a different region of the green and yellow VO Lumes. Therefore, any activation in the first slice of the yellow volume means that a-face is detected, independently of T He face location. Then the fully-connected layer was responsible to ' translate ' a face and a human body. In both examples, an activation is received at one of the fully-connected n

complete code below is my Stanford machine learning tutorial, Which I typed myself: Import numpy as np ''' reference: http://ufldl.stanford.edu/wiki/index.php/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C'''class NeuralNetworks (object): ''' def _ init _ (self, n_layers = None, active_type = None, n_iter = 10000, error = 0.05, alpha = 0.5, lamda = 0.4): '''build a neural networ