deeplearning ai

After three years of crazy brush theory, I thought it was time to stop and do something useful. So I decided to write them down in kaibo. First, I tried to sort out the learned theories, second, supervise yourself and share with you. Let's talk about deeplearning first, because it has a certain degree of practicality (people say "It's very close to money"), and many domestic bulls have talked about it too, I try to explain it in other ways.

DeepLearning tutorial (3) MLP multi-layer awareness machine principle + code explanation, deeplearningmlp DeepLearning tutorial (3) MLP multi-layer sensor principle + code explanation @ Author: wepon @ Blog: http://blog.csdn.net/u012162613/article/details/43221829 This article introduces the multi-layer sensor algorithm, especially the code implementation. Based on python theano, the Code comes from Multil

stationary state to a moving state at any time, or vice versa, neither of these modes seems appropriate. Intelligent autofocus is a camera automatically selects the focus mode according to the state of the subject (stationary or motion), which combines single-shot AF and continuous autofocus to resolve the above mentioned problems, thus making it more suitable for use in situations where the object is stationary. It should be noted that the first two mentioned autofocus methods are the most

I am little white, said not very good, please forgive@author: Wepon@blog: http://blog.csdn.net/u012162613/article/details/43169019Reference: Pickle-python object serialization, Deeplearning Getting startedOne, Python read "***.pkl.gz" fileUsing the gzip and cpickle modules in Python, simply use the code below and refer to the links given above if you want to learn more about them.[Python]View PlainCopy #以读取mnist. pkl.gz as an example Imp

Deeplearning library is quite a lot of, now GitHub on the most hot should be caffe. However, I personally think that the Caffe package is too dead, many things are packaged into a library, to learn the principle, or to see the Theano version.My personal use of the library is recommended by Friends Keras, is based on Theano, the advantage is easy to use, can be developed quickly.Network frameworkThe network framework references Caffe's CIFAR-10 framew

matrix. PCA Whitening with regularisation% resultsincha covariance matrix with diagonal entries starting close to%1and gradually becoming smaller. We'll verify these properties here.%Write code to compute the covariance matrix, Covar.Without regularisation (SetEpsilon to0or close to0), % when visualised asAn image, you should see a red line across the%Diagonal (one entries) against a blue background (zero entries).%with regularisation, you should see a red line that slowly turns%blue across the

);% M2 isThe predicted matrixM2=Bsxfun (@rdivide, M2, sum (M2));%1{·}operatorOnly preserve a part of positions of log (M2) M= Groundtruth. *log (M2); cost= -(1/numcases) * SUM (SUM (M)) +Weightdecay;%Thetagradthetagrad=zeros (numclasses, inputsize);%difference between ground truth and predict Valuediff= Groundtruth-M2; forI=1: Numclasses Thetagrad (i,:)= -(1/numcases) * SUM (data. * Repmat (diff (I,:), inputsize,1)) ,2)'+ lambda * theta (i,:);End%-------------------------------------------------

and Francisco GuzmanValidating and extending Semantic knowledge Bases using Video games with a PurposeDaniele Vannella, David Jurgens, Daniele Scarfini, Domenico Toscani and Roberto NavigliVector space semantics with frequency-driven motifsShashank Srivastava and Eduard HovyWeak Semantic context helps phonetic learning in a model of infant language acquisitionStella Frank, Naomi Feldman and Sharon GoldwaterWeakly supervised User profile Extraction from TwitterJiwei Li, Alan Ritter and Eduard Ho

thought of as C.2. The cost can be written as a function of the neural network output:For a separate training sample x its two-time cost function can be written:X, y are fixed parameters and are not changed by weight and bias, meaning that this is not an object of neural network learning, so it is reasonable to treat C as a function with only the output activation value of al .Four basic equations for reverse propagationConcept:ΔJL: The error on the jth neuron of the lth layer.1. Output error e

In the deep network, the learning speed of different layers varies greatly. For example: In the back layer of the network learning situation is very good, the front layer often in the training of the stagnation, basically do not study. In the opposite case, the front layer learns well and the back layer stops learning.This is because the gradient descent-based learning algorithm inherently has inherent instability, which causes the learning of the front or back layer to stop.Vanishing gradient p

Before I have been using Theano, the previous five deeplearning related articles are also learning Theano some notes, at that time already feel Theano use up a little trouble, sometimes want to achieve a new structure, it will take a lot of time to programming, so think about the code modularity, Easy to reuse, but because it's too busy to do it. Recently discovered a framework called Keras, which coincides with my ideas, is particularly simple to use

*log (sparsityparam./rho) + (1-sparsityparam) *log ((1-sparsityparam)./(1-rho ));%Compute weight Decay termtempW1= W1. *w1;tempw2= W2. *W2; WD= (lambda/2) * (SUM (sum (tempW1)) +sum (sum (tempW2));= Cost./m + WD +Klpen; W1grad= W1grad./m + Lambda. *W1; W2grad= W2grad./m + Lambda. *W2;b1grad= B1grad./M;b2grad= B2grad./m;%-------------------------------------------------------------------%3. Update the Parametersafter computing the cost and gradient, we 'll% convert the gradients back to a vector

:6868/, enter the password, you can enter the Ipython notebook.If you need to keep the connection,Nohup Ipython Notebook--profile=myserverKill the ConnectionLsof Nohup.outKill-9 "PID"Completed!The final hardware configuration:Cpu:intel X99 Platform i7 5960KMemory: DDR4 2800 32G (8g*4)Motherboard: GIGABYTE X99-UD4Video card: GTX Titan XHard disk: ssd+ ordinary hard diskSystems and SoftwareOperating system: Ubuntu 14.04.3 x64cuda:7.5Anaconda 2.3Theano 7.0Keras 2.0Resources:http://timdettmers.com/2

Exercise:vectorizationLinks to Exercises:exercise:vectorizationNote the point:The pixel points of the mnist picture have been normalized.If you re-use the SAMPLEIMAGES.M in Exercise:sparse Autoencoder for normalization,The visual weights that will result in the training are as follows:My implementation:Changing the parameter setting of TRAIN.M and selecting the training samplePercent STEP0: Here we provide the relevant parameters values that would% allow your sparse autoencoder toGetGood filters

gradient descent algorithm to a normalized neural networkThe partial derivative of the normalized loss function is obtained:You can see the paranoid gradient drop. Learning rules do not change:And the weight of learning rules has become:This is the same as normal gradient descent learning rules, which adds a factor to readjust the weight of W. This adjustment is sometimes called weight decay .Then, the normalized learning rule for the weight of the random gradient descent becomes:The normalized

Wunda deeplearning Image style conversion, image style conversion data image style conversion data Deep learning art:neural style Transfer Welcome to the second assignment of this week. In this assignment, you'll learn about neural Style Transfer. This algorithm is created by Gatys et al. (https://arxiv.org/abs/1508.06576). in this assignment, you'll:-Implement the neural style transfer algorithm-Generate novel artistic images using your algorithm Mo

TensorFlow Introductory Tutorials 0:bigpicture The speed of introduction TensorFlow Introductory Tutorial 1: Basic Concepts and understanding TensorFlow Getting Started Tutorial 2: Installing and Using TensorFlow Introductory Tutorials The basic definition of 3:CNN convolution neural network understanding TensorFlow Getting Started Tutorial 4: Realizing a self-created CNN convolution neural network TensorFlow Introductory tutorials for 5:tensorboard panel visualization management A simple

Let us think about the distant past, what is the reason why we abandon the same function machine, the choice of smart phone?Is it because of the beauty value? Interactive freshness? I believe that the vast majority of users, because the app mode brings too much practical value, people around the use of, and they can not even follow up. So amid the greatness of the Lord is not only to subvert the shape of the phone, but more importantly to the future of the mobile phone ecosystem opened the entra

Long knowledge of games ------ game AI Based on Behavior tree and state machine (I), ------ ai Sun Guangdong 2014.6.30 AI. Our first impression may be robots, which are mainly about applications in games. Modern computer games have already incorporated a large number of AI elements. The interactions we make when playi

Happy New Year! This is a collection of key points of AI and deep learning in 2017, and ai in 2017RuO puxia Yi compiled from WILDMLProduced by QbitAI | public account QbitAI 2017 has officially left us. In the past year, there have been many records worth sorting out. The author of the blog WILDML, Denny Britz, who once worked on Google Brain for a year, combed and summarized the