cmu machine learning course

An introductory tutorial on machine learning with a higher degree of identity, by Andrew Ng of Stanford. NetEase public class with Chinese and English subtitles teaching video resources (http://open.163.com/special/opencourse/ machinelearning.html), handout stamp here: http://cs229.stanford.edu/materials.htmlThere are a variety of similar course

only need to use the sudo apt-get install virt-manager to install the software. The software relies on Libvirt and is automatically installed during the installation process. The effect of running Virt-manager is, note that you must run with sudo because the software requires Superuser privileges:The software automatically identifies whether the virtual machine environment in the system is QEMU+KVM or Xen. Create a new virtual

Coursera Andrew Ng Machine learning is really too hot, recently had time to spend 20 days (3 hours a day or so) finally finished learning all the courses, summarized as follows:(1) Suitable for getting started, speaking the comparative basis, Andrew speaks great;(2) The exercise is relatively easy, but to carefully consider each English word, or easy to make mist

./////////////////////////////////////////////////////////////////////////////////////////////////////////////// //////////////////////////////////////The following content is referenced: http://blog.csdn.net/zouxy09/article/details/20319673Logistic regression (logisticregression)Logistic regression (logistic regression) is the most commonly used machine learning method in the industry to estimate the likel

prediction Naturual Language Processing Coursera Course Book on NLP NLTK NLP W/python Foundations of statistical Language processing Probability Statistics Thinking Stats-book + Python Code From algorithms to Z-scores-book The Art of R Programming-book (not finished) All of Statistics Introduction to statistical thought Basic probability theory Introduction to probability Principle of u

This blog is reproduced from a blog post, introduced Gan (generative adversarial Networks) that is the principle of generative warfare network and Gan's advantages and disadvantages of analysis and the development of GAN Network research. Here is the content. 1. Build Model 1.1 Overview Machine learning methods can be divided into generation methods (generative approach) and discriminant methods (discrimin

After talking about the tree in the data structure (for details, see the various trees in the data structure in the previous blog post), let's talk about the various tree algorithms in machine learning algorithms, including ID3, C4.5, cart, and the tree model based on integrated thinking Random forest and GBDT. This paper gives a brief introduction to the basic ideas of various tree-shape algorithms, and fo

, i.e. gt,i=gt,i+n (0,σ2t) The variance of the Gaussian error requires annealing: σ2t=η (1+t) γ increasing the random error on the gradient increases the robustness of the model, even if the initial parameter values are not chosen well and is suitable for training in a particularly deep-seated network. The reason for this is that increasing random noise is more likely to jump over local extreme points and find a better local extremum, which is more common in deep networks. Summary in the above

course, there are many improvements to this disadvantage ). The core idea of bovw is as follows. Some people have asked, there are many methods to extract image features, such as sift Feature Extraction and star feature extraction. Why do we need to use bovw models to characterize the image? Because of Sift, the feature vectors obtained by star feature extraction machines are multidimensional. For example, the sift vectors are 128 dimensions, and an

Tags: probability gradient drop RAM log directory UNC measure between playFinishing the Machine Learnig course from Andrew Ng Week1Directory: What is machine learning Supervised learning Non-supervised learning

Starting today to learn machine learning, mainly in several aspects, is machine learning for my personal several aspects of the promotion is particularly large. Whether it's a financial or an image.In Finance I need machine learning