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This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This course is a choice for those who want to understand and master machine learning. This course covers some of the basic concepts and methods of machine learning, and the programming of this course plays a huge role in mastering th
This paper mainly records the cost function of neural network, the usage of gradient descent in neural network, the reverse propagation, the gradient test, the stochastic initialization and other theories, and attaches the MATLAB code and comments of the relevant parts of the course work. Concepts of neural networks,
This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This course is a choice for those who want to understand and master machine learning. This course covers some of the basic concepts and methods of machine learning, and the programming of this course plays a huge role in mastering th
)/m; at End - End - -%size (J,1) -%size (J,2) - ind3 = A3-Ty; -D2 = (D3 * THETA2 (:,2: End)). *sigmoidgradient (z2); toTheta1_grad = Theta1_grad + d2'*a1/m; +Theta2_grad = Theta2_grad + d3'*a2/m; - the% ------------------------------------------------------------- *jj=0; $ Panax Notoginseng forI=1: Size (Theta1,1) - forj=2: Size (Theta1,2) theJJ = JJ + Theta1 (i,j) *theta1 (i,j) *lambda/(m*2); + End A End theSize (Theta1,1); +Size (Theta1,2); - $ forI=1: Size (THETA2,1) $
Andrew Ng personal homepage, http://www.andrewng.org/, his team's recent papers are organized as follows: April 2018: Noising and denoising natural language: diverse backtranslation for grammar correction April 2017: Chexnet: radiologist-level ppneumonia onia detection on chest x-rays with deep learning Cardiologist-level arrhythmia detection with convolutional Neural
. Regular items are generally used at the time of overfitting, simply speaking is the limit of the weight of the size, L2 is the sum of the weights of the square and added to the target function, and finally the weight tends to smooth, there is a regular term is L1, can make weights tend to sparse, that is, many of the 0. Change the network structure is generally a relatively fine adjustment, such as activation from Relu to Prelu, a layer of convolution core size changes, and so on, general smal
state of the moment. Through the "Forgotten Gate" and "Input Gate", the LSTM structure can effectively determine what information should be forgotten and what information should be retained. LSTM The current moment state $C _t$, the output of the current moment needs to be generated, which is done through the "Output gate". GRU's two doors: one is the "Update Gate", which merges LSTM's "Forgotten Gate" and "Input gate" into a "gate" structure, and the other is the "Reset Gate". "Visually, the
network prediction Total number of layers $L $-neural network (including input and output layers) $\theta^{(L)}$-the weight matrix of the $l$ layer to the $l+1$ layer $s _l$-the number of neurons in the $l$ layer, note that $i$ counts from 1, and the weights of bias neurons are not counted in the regular term. The number of neurons in the _{l+1}$-layer of the $s $l+1$ Reference documents[1] A
continuously updating theta. Map Reduce and Data Parallelism: Many learning algorithms can be expressed as computing sums of functions over the training set. We can divide up batch gradient descent and dispatch the cost function for a subset of the data to many different machines So, we can train our algorithm in parallel. Week 11:Photo OCR: Pipeline: Text detection Character segmentation Character classification Using s
fairly trivial to validate. Depicting the approximation of an ' undocumented ' function as a black-box are most probably a fundamentally flawed idea in I Tself. If we equate this with the biological thought process, the signals and the corresponding trained behavior, we had an EXPE CTED output based on the training set as an observer. However, the non-identifiable model, the approximation provided by the neural network are fairly impenetrable for all
(Original address: Wikipedia)Introduction:Pulse Neural Network spiking Neuralnetworks (Snns) is the third generation neural network model, the simulation neuron is closer to reality, besides, the influence of time information is considered. The idea is that neurons in a dynamic neural network are not activated in every iteration of the transmission (whereas in a
This series of articles is the study notes of "machine learning", by Prof Andrew Ng, Stanford University. This article is the notes of week 5, neural Networks learning. This article contains some topic on cost Function and backpropagation algorithm.Cost Function and BackPropagationNeural
BP algorithm of neural network, gradient test, random initialization of Parameters neural Network (backpropagation algorithm,gradient checking,random initialization)one, cost functionfor a training set, the cost function is defined as:where the red box is circled by a regular term, K: the number of output units is the number of classes, L: The total number of neural
**************************************Note: This blog series is for bloggers to learn the "machine learning" course notes from Professor Andrew Ng of Stanford University. Bloggers deeply learned the course, do not summarize is easy to forget, according to the course plus their own to do not understand the problem of the addition of this series of blogs. This blog series includes linear regression, logistic
Linear regression and logistic regression are sufficient to solve some simple classification problems, but in the face of more complex problems (such as identifying the type of car in the picture), using the previous linear model may not result in the desired results, and due to the larger data volume, the computational complexity of the previous method will become unusually large. So we need to learn a nonlinear system: neural networks.When I was stu
, Jan "Honza" Cernocky, Sanjeev Khudanpur,Extensions of recurrent neural N Etwork Language Model, ICASSP [Paper] Stefan Kombrink, Tomas Mikolov, Martin karafiat, Lukas burget, recurrent neural Network based Language Modeling in Mee Ting recognition, Interspeech [Paper] Speech recognition Geoffrey Hinton, Li Deng, Dong Yu, George E. Dahl, Abdel-rahman Mohamed, Navdeep jaitly,
in Google, if the landing Google is difficult to come here to provide you with a stable landing method, one months 10 yuan is not expensive.(1) Ngiam, Jiquan,koh Pang wei,chen Zheng hao,bhaskar sonia,ng Andrew Y. Sparse Filtering,[c]. Advances in Neural information processing Systems 24:25th annual Conference on Neural
, where RIt is a learning rate set by yourself. If it is too large, it will cause learning shaking. The inverted triangle is the gradient. In addition, the output layer does not have to use the objective functions (Figure 6). You can specify different objective functions as needed, even if you add an support vector machine to the final output, as long as you can perform the export, just get the gradient. In fact, one of Hinton's disciples is doing this recently. I use my own wisdom to improve th
1 / 35 , the variation of each weight is +20,+50,+30, thus obtaining a new weight vector (70, 100, 80).The Delta-rule is given:In fact, this is the perception machine, which we have learned in Andrew Ng's course. The weighted vector obtained by iteration may not be perfect, but it should be a solution that makes the error small enough. If the learning step is small enough and the learning time is long enough, t
-think that ' s the much cooler application). Training a language model on Shakespeare allows us-generateshakespeare-like text.this fun postby ; Andrew karpathydemonstrates what Character-level language models Basedon Rnns is capable of. I ' m assuming that's somewhat familiar with basic neural Networks. If you're not, want to head through implementing A
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