coursera neural networks

Neural NETWORKS, part 1:backgroundArtificial Neural Networks (NN for short) is practical, elegant, and mathematically fascinating models for machine LearniNg. They is inspired by the central nervous systems of humans and animals–smaller processing units (neurons) is connected Together to form a complex network which is

ManualNeural Network (ANN)It is an important branch of AI. After decades of development, artificial neural networks have been widely applied to business problems in the real world. Artificial neural networks can be widely used in Machine Fault Diagnosis, medical diagnosis, speech recognition, and securities management.

**************************************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 regression, neural network, machine learning

The fourth lecture of Professor Geoffery Hinton's Neuron Networks for machine learning mainly describes how to use the back propagation algorithm to learn the characteristic representation of a vocabulary.Learning to predict the next wordThe next few sections focus on how to use the back propagation algorithm to learn the feature representation of a vocabulary. Starting with a very simple example, we introduce the use of the back propagation algorithm

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

weight of the Hidden Layer Change1 = 0.9294*0.76 = 0.7063 Wo1 = original wo1 + Change1 *0.5 = 0.1 + 0.7063*0.5 = 0.4532 Change2 =-0.0764*0.76 =-0.0581 WO2 = original WO2 + Change2 *0.5 = 0.1-0.0571*0.5 = 0.0419 Similarly, wo3=0.0419 Update the input weight of the Hidden Layer Change1 = 0.1839*1 = 0.1839 Wi1 = original wi1 + Change1 *0.5= 0.5 + 0.09195 = 0.5920 Similarly, wi2 = 0.5920 After the weight is updated, the neural network

. The artificial intelligence technology in game programming (serial one) Introducing neural networks in normal language(Neural Networks in Plain 中文版) Because we do not have a good understanding of the brain, we often try to use the latest technology as a model to explain it. When I was a child, we all beli

"Recurrent convolutional neural Networks for Text classification" Paper Source: Lai, S., Xu, L., Liu, K., Zhao, J. (2015, January). Recurrent convolutional neural Networks for Text classification. In Aaai (vol. 333, pp. 2267-2273). Original link: http://blog.csdn.net/rxt2012kc/article/details/73742362 1. Abstract Te

IntroductionIn the previous chapter, although the BP neural network has made great progress, but it has some unavoidable problems, one of which is more confused is the problem of local optimal solution. It is risky to touch only those things you already like, that you may be involved in a self-centered whirlpool that ignores anything that is slightly different from your standards, even if you would have liked it. This phenomenon is known as t

(deep) Neural Networks (deep learning), NLP and Text MiningRecently flipped a bit about deep learning or common neural network in NLP and text mining aspects of the application of articles, including Word2vec, and then the key idea extracted out of the list, interested can be downloaded to see:Http://pan.baidu.com/s/1sjNQEfzI did not put some of my own ideas into

Application examples of RNN--a language model based on RNN Now, let's introduce a model based on the RNN language. We first input the word into the recurrent neural network, each input word, the recurrent neural network output so far, the next most likely word. For example, when we enter in turn: I was late for school yesterday. The output of the neural networ

theoretical knowledge : Deep learning: 41 (Dropout simple understanding), in-depth learning (22) dropout shallow understanding and implementation, "improving neural networks by preventing Co-adaptation of feature detectors "Feel there is nothing to say, should be said in the citation of the two blog has been made very clear, direct test itNote :1. During the testing phase of the model, the output of the hid

We can pass the torch. NN package constructs a neural network. Now we've learned that AUTOGRAD,NN defines models based on Autograd and differentiates them.Onenn.Module模块由如下部分构成：若干层，以及返回output的forward(input)方法。For example, this diagram depicts a neural network for digital Image classification:This is a simple feedforward (feed-forward) network that reads input content, each layer accepts inputs from the prev

convolutional neural Network Origin: The human visual cortex of the MeowIn the 1958, a group of wonderful neuroscientists inserted electrodes into the brains of the cats to observe the activity of the visual cortex. and infer that the biological vision system starts from a small part of the object,After layers of abstraction, it is finally put together into a processing center to reduce the suspicious nature of object judgment. This approach runs coun

to stop training.Limiting the size of the weightsThis section describes how to control the capacity of a network by limiting the size of the weights, and the standard method is to introduce a penalty to prevent the weights from becoming too large. Along with some implicit assumptions, neural networks with small weights are much simpler than power values. We can use several different methods to limit the we

"Convolutional neural Networks-evolutionary history" from Lenet to Alexnet This blog is "convolutional neural network-evolutionary history" of the first part of "from Lenet to Alexnet" If you want to reprint, please attach this article link: http://blog.csdn.net/cyh_24/article/details/51440344 More related blog please poke: http://blog.csdn.net/c

object always correspond to the same block of standard pixels of the image. In addition, the box can provide invariance for many different degrees of freedom: translation, rotation, scale, shear, stretch, and so on. However, it is very difficult to choose a box, because there may be some problems such as segmentation error, covering, singular angle of view and so on.The method of brute force generalization (the Brute forces normalization approach) is given.The third and fourth methods are descr

Refer to:The unreasonable effectiveness of recurrent neural NetworksRecurrent neural Networks sequences . Depending on your background you might being wondering: What makes recurrent Networks so special ? A glaring limitation of Vanilla neural

Welcome reprint, Reprint Please specify: This article from Bin column Blog.csdn.net/xbinworld.Technical Exchange QQ Group: 433250724, Welcome to the algorithm, technology interested students to join.Recently, the next few posts will go back to the discussion of neural network structure, before I in "deep learning Method (V): convolutional Neural network CNN Classic model finishing Lenet,alexnet,googlenet,vg