coursera neural networks

convolutional Neural Networks (convolutional neural Network): A type of classifier that uses neural networks to train parameters from data, extract features, pre-determine convolution kernel size, initialize randomly, and after feedback adjustment, different convolution core

In 2006, Geoffery Hinton, a professor of computer science at the University of Toronto, published an article in science on the use of unsupervised, layer-wise greedy training algorithms based on depth belief networks (deep belief Networks, DBN). has brought hope for training deep neural networks.If Hinton's paper, published in the journal Science in 2006, [1] is

AlexNet: (ILSVRC Top 5 test error rate of 15.4%) the first successful display of the convolutional neural network potential network structure. key point: with a large amount of data and long-time training to get the final model, the results are very significant (get 2012 classification first) using two GPU, divided into two groups for convolution. Since Alexnet, convolutional neural

net.initialize (), we will use the default random initialization method of mxnet. When initializing under default conditions, each element of the weight parameter is randomly sampled in a uniform distribution between 0.07 and 0.07, and all elements of the deviation parameter are zeroed.Xavier Random InitializationThere is also a more commonly used random initialization method called Xavier Random initialization, assuming that the input number of an all-connected layer is: math:a, the output num

visual comprehension of convolutional neural networks The first to suggest a visual understanding of convolutional neural Networks is Matthew D. Zeiler in the visualizing and understanding convolutional Networks. The following two blog posts can help you understand this a

music, so the best algorithm convergence after the test . Many of the world's documents I've tested are like strum.2. Shortly after the start of the project, there is a forum dedicated to exchanging learning experiences and questions, point here. The above comment is the problem I encountered, if you encounter a new problem, you can post to the forum for help. I see some people generate music that has that weird Gothic-style haha.3. The specific principles behind this project I did not write, o

This paper summarizes some contents from the 1th chapter of Neural Networks and deep learning.learning with gradient descent algorithm (learning with gradient descent)1. TargetWe want an algorithm that allows us to find weights and biases so that the output y (x) of the network can fit all the training input x.2. Price functions (cost function)Define a cost function (loss function, objective function): The

Label: style blog HTTP color ar SP 2014 art log 1. Basic Structure of Neural Networks Neural Network: N inputs, m middle layers, and K output layers X indicates the input, W indicates the input weight to the middle layer, V indicates the weight from the middle layer to the output, and y indicates the network output. Threshold indicates the threshold of the in

Scalable Object Detection using deep neural Networksauthor : Dumitru Erhan, Christian szegedy, Alexander Toshev, and Dragomir Anguelovreferences : Erhan, Dumitru, et al. "Scalable object detection using deep neural networks." Proceedings of the IEEE Conference on computer Vision and Pattern recognition. 2014.citations : 181 (Google scholar, by 2016/11/23).Project

evolution of deep neural networks in image recognition applications"Minibatch" You use a data point to calculate to modify the network, may be very unstable, because you this point of the lable may be wrong. At this point you may need a Minibatch method that averages the results of a batch of data and modifies it in their direction. During the modification process, the change intensity (learning rate) can b

Read the Web page found that to learn deep learning, should be first on convolutional neural network (convolutional neural Networks, referred to as CNN), convolutional Neural network was proposed by Yann LeCun, LeCun joined Facebook at the end of 2013 to take charge of the newly established AI Lab.Here are some more in

Understanding the difficulty of training deep feedforward Neural Understanding the difficulty of training deep feedforward Neural Networks Overview Sigmod experiment cost function influence weights initialization Summary Neural networks are difficult to train, and the autho

Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." Nature 529.7587 (2016): 484-489.Alphago's thesis, the main use of the RL technology, do not know before the use of RL to do Weiqi.Proposed two networks, one is the strategy network, one is the value network, all through the self-battle realization.Policy Network:The strate

is: F ' (x) = f (x) * (1-f (x)), calculation is very convenient. The code is as follows:1%%compute gradients using backpropagation2 3%%% YOUR CODE here%%%4%Output Layer5Output =Zeros (Size (Pred_prob));6Output (Index) = 1;7Error = Pred_prob-output;8 9 forL = numhidden+1: -1:1Tengradstack{l}.b = SUM (error,2); One if(L = = 1) AGradstack{l}. W = Error * Data'; - Break; - Else theGradstack{l}. W = error * HACT{L-1}'; - End -Error = (Stack{l}. W'*error. * HACT{L-1}. * (1-hact{l-1}

,x_test) Y_prediction_train = Predict (W,b,x_train) # # # # END CODE here # # # print train/test Errors print ("Train accuracy: {}% ". Format (100-np.mean (Np.abs (y_prediction_train-y_train) *)) print (" Test accuracy: {}% ". Format (100-np.me An (Np.abs (y_prediction_test-y_test)) *) d = {"Costs": costs, "y_prediction_test": Y_prediction_test, "Y_prediction_train": Y_prediction_train, "W": W, "B": B, "learning_rate": Learnin G_rate, "Num_iteRations ": num_iterations} print (d[" costs

\):The chain rules are updated as follows:\[\begin{split}\frac{c_0}{\partial \omega_{jk}^{(L)}}= \frac{\partial z_j^{(L)}}{\partial \omega_{jk}^{(l)}}\ Frac{\partial a_j^{(L)}}{\partial z_j^{(l)}}\frac{\partial c_0}{\partial a_j^{(L)}}\=a^{l-1}_k \sigma\prime (z^ {(l)}_j) 2 (a^{(l)}_j-y_j) \end{split}\]And to push this formula to other layers ( \frac{c}{\partial \omega_{jk}^{(L)}}\) , only the \ (\frac{\partial c}{\partial a_j^{) in the formula is required ( L)}}\) .Summarized as follows:Therefo

useful when combined with a number of different random subsets of other neurons. The first two fully connected layers use dropout. Without dropout, our network would show a lot of overfitting. The dropout increases the number of iterations required for convergence by roughly one-fold.4. Image preprocessing① size NormalizationTo 256x256 all the pictures to the size of the scale, as for why not directly normalized to 224 (227), please refer to the above-mentioned expansion of the dataset operatio

1 Introduction In this article, we will introduce a framework aforge using C #, which allows you to easily manipulate artificial networks, computer vision, machine learning, image processing, genetic algorithms, etc. Introduction of 2 neural network design part framework Here, I want to emphasize: This piece of code is very beautiful, a code such as poetic beauty, let me charmed. This piece of code is i

Record some of the small points in the neural network blob dimensions in 1 caffe The BLOBs in Caffe have 4 dimensions, respectively num,channel,width and height; In which we define each network layer, a commonly used parameter numout, is the designated channel; For example, the dimension is 1*3*5*5 data input network (that is, each time input a 5*5 size 3-channel graph), after a stride for 2,pad 1,kernel for 2,numout to 2 of the convo

Https://stats.stackexchange.com/questions/164876/tradeoff-batch-size-vs-number-of-iterations-to-train-a-neural-networkIt had been observed in practice, when using a larger batch there was a significant degradation in the quality of T He model, as measured by it ability to generalize.https://stackoverflow.com/questions/4752626/epoch-vs-iteration-when-training-neural-netw