coursera 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

http://blog.csdn.net/pipisorry/article/details/4397356Machine learning machines Learning-andrew NG Courses Study notesNeural Networks Representation Neural network representationnon-linear Hypotheses Nonlinear hypothesisNeurons and the brain neurons and brainsModel representation models representExamples and intuitions examples and intuitive knowledgeMulticlass Classification Multi-class classificationfrom:

self.nodesinLayers.append (int (SELF.OUTPUTDI)) #self. nodesinb=[] #self. nodesinb + = self. Nodesinhidden #self. Nodesinb.append (int (SELF.OUTPUTDI)) #for element in Self.nodesinlayers: #self . Nodesinlayers=int (Self.nodesinlayers[idx]) #weight matrix, it's a list and each element is a numpy matrix # Weight matrix, here are Wij, and in BP we could inverse it into Wji #here we store the matrix as Numpy.array SE Lf.weightmatrix=[] Self. B=[] for IDX in range (0,self.NL-1): #Xaxier ' s scaling

Adit DeshpandeCS undergrad at UCLA (' 19)Blog Abouta Beginner ' s Guide to Understanding convolutional neural Networks Part 2IntroductionLink to Part 1In this post, we'll go to a lot more of the specifics of Convnets. Disclaimer: Now, I did realize that some of these topics is quite complex and could be made in whole posts by themselves. In a effort to remain concise yet retain comprehensiveness, I'll provi

Reference: Artificial neural network-Han Liqun pptlooking at some of the language models based on neural networks, compared with traditional language models, there is no need for additional smoothing algorithms In addition to the amount of computational effort, which makes them surprisingly effective. These networks 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

in the second layer.The formula is:The original image is mapped to, 0-255, here is generally set to 8, function h is a step function. , which indicates the number of filters in the second layer.For each output matrix of the first layer, it is divided into B block, calculate the histogram information of each block, then cascade the histogram features of each block, and finally get the Block expansion histogram feature:Overlapping and non-overlapping block patterns can also be used for histogram

next layer, each neuron only related to the K values of the previous layer.However, the introduction of the concept of weight sharing, the model is further simplified to achieve: the number of weight is only related to the size of kernel. For kernel and Weight sharing, it can be understood that there is no fixed connection between the L layer and the L-1 layer, but rather dynamic binding, where there is a small window between the two layers, called kernel. A small portion of the original image

5.1 Cost FunctionSuppose the training sample is: {(x1), Y (1)), (x (2), Y (2)),... (x (m), Y (m))}L = Total No.of layers in NetworkSl= no,of units (not counting bias unit) in layer LK = number of output units/classesThe neural network, L = 4,S1 = 3,s2 = 5,S3 = 5, S4 = 4Cost function for logistic regression:The cost function of a neural network:　　　5.2 Reverse Propagation Algorithm backpropagationA popular ex

This is a creation in Article, where the information may have evolved or changed. Http://pan.baidu.com/s/1hr3kxog http://download.csdn.net/detail/nehemiah666/9472669 There are nature on the paper, I translated the Chinese version, and recorded a narration alphago working principle of the video, is a summary of the principle of alphago work. Here is the summary section: For artificial intelligence, Weiqi has always been considered the most challenging classic game, due to its huge search space

Idea: Using RNN to model users ' browsing order, using FNN to simulate CF, two networks learning togetherRNN Network structure:The state of the output layer represents a page that a user browses, which can be seen as a one-hot representation, and STATE0 to 3 is the page that is browsed in turn. Because RNN input number is limited, if the user browses too many pages, then will lose the first of those pages, paper in order to retain this part of the inf

of the word vector effect is also possible.Channel (Channels): An image can take advantage of (R, G, B) as a different channel, while the input channel of the text is usually a different way of embedding (such as Word2vec or glove), In practice, the use of static word vectors and fine-tunning-word vectors as different channel methods are also used.One dimensional convolution (conv-1d): The image is a two-dimensional data, the word vector expression of the text is one-dimensional data, so in tex

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