udemy machine learning course review

Tags: deviation chinese data cts You multitasking performance GPO ESCLearning Goals Understand what multi-task learning and transfer learning is Recognize bias, variance and data-mismatch by looking in the performances of your algorithm on train/dev/test sets "Chinese Translation"Learning GoalsLearn what multi-tasking

, 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

algebra review, I'll be the using one index vectors. Most vector subscripts in the course start from 1.When talking on machine learning applications, sometimes explicitly say if we need to switch to, when we need to use The zero index vectors as well. Discussion of machine

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Cost Function ( Cost functions ) The last lecture introduced the multiclass classification problem. The difference between the multiclass classification problem and the binary classification problem lies in that there are multiple output units, which are summarized as follows: At the same time, we also know the price functions of Logistic regress

Original: Http://www.infoq.com/cn/news/2014/03/baidu-salon48-summaryMarch 15, 2014, in the 48th phase of Baidu Technology salon, sponsored by @ Baidu, @InfoQ responsible for organizing and implementing, from Baidu Alliance Big Data Machine Learning technology responsible for summer powder, and Sogou precision Advertising Research and development Department of technical manager Wang Xiaobo, each sharing its

A survey of data cleansing and feature processing in machine learning with the increase of the size of the company's transactions, the accumulation of business data and transaction data more and more, these data is the United States as a group buying platform of the most valuable wealth. The analysis and mining of these data can not only provide decision support for the development direction of the American

represent the right side of the inequality and Delta to represent ε. So we have: We have previously studied the probability of occurrence of bad events. Now let's look at the probability of occurrence of optimistic events: P [| ein (G)-eout (G) | Use Ω (n, H, Delta) instead of ε to get the desired good event definition: | eout-Ein | Ω is positively related to N, Delta, and h or VC. We ignore the Ω parameter first, so there are: | eout-Ein | In most cases, eout is larger than EIN, because w

+ 1 parameter: x0 -- x256. We hope to use machine learning to determine the values of all these parameters. However, with so many parameters, machine learning may take a lot of time to complete, and the effect is not necessarily good. We can see that some pixels are not needed, so we should extract some features from

Finally the end of the final, look at others summary: http://blog.sina.com.cn/s/blog_641289eb0101dynu.htmlContact Machine Learning also has a few years, but still only a rookie, when the first contact English is not good, do not understand the class, what things are smattering. After learning some open classes and books on the go, I began to understand some conce

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Open Course address: https://class.coursera.org/ml-003/class/index INSTRUCTOR: Andrew Ng1. unsupervised learning introduction (Introduction to unsupervised learning) We mentioned one of the two main branches of machine learning-supervised

hypothesis closest to F and F. Although it is possible that a dataset with 10 points can get a better approximation than a dataset with 2 points, when we have a lot of datasets, then their mathematical expectations should be close and close to F, so they are displayed as a horizontal line parallel to the X axis. The following is an example of a learning curve: See the following linear model: Why add noise? That is the interference. The purpose is to

Hope to learn the gospel of the Children of Learning machine, the world's largest AI company Google launched a "machine learning Crash Course", not only the whole Chinese, but also free to listen to OH. The course is 15 hours, th

calculates the accuracy of the entire system at this time: As shown in, text recognition consists of four parts. Now we can find the system accuracy after optimization for each part. The question is, how can we improve the accuracy of the entire system? We can see from the table that, if we have optimized the text moderation part, the accuracy will be72%Add89%If we optimize the character segmentation, the accuracy is only from89%To90%If character recognition is optimized90%To100%In contr

learning.In fact, these two states are not completely divided, for example, if we are trading in a lot of fraud, then we study the problem from anomaly detection to supervise learning.Exercise: Intuitive judgment of two situationsChoosingwhat Features to useThe previous approach is to assume that the data satisfies the Gaussian distribution, and also mentions that if the distribution is not Gaussian distribution, the above method can be used, but if we convert the distribution to approximate Ga

This is a machine learning course that coursera on fire, and the instructor is Andrew Ng. In the process of looking at the neural network, I did find that I had a problem with a weak foundation and some basic concepts, so I wanted to take this course to find a leak. The current plan is to see the end of the neural netw

Tags: machine learning, data mining, overfitting, deterministic noiseCourse introductionThis section describes the problem of over-generalization in machine learning. The author points out that one of the ways to differentiate a professional-level player from a hobbyist is how they deal with the problem of preparation.

post-pruning algorithm (its disadvantage is that it is computationally large), with the minimum expected cost of miscalculation (ECM) and the minimum description length (DML) algorithm. A post-pruning algorithm is described below, which determines whether to merge leaf nodes based on the test data and the error size:Split the test data for the given tree:If The Eithersplit is a tree:call prune on that splitCalculate theerror associated with merging leaf nodesCalculate Theerror without mergingIf

does not introduce a matrix, which is easy to calculate and can be correctly executed if there are few samples. The multi-element model is complex to calculate after the matrix is introduced. to calculate the inverse of the matrix, the model must be executed when the sample value is greater than the feature value. ------------------------------------------Weak split line---------------------------------------------- Although exception detection is mentioned in this article, it is used to in

This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL: https://www.coursera.org/learn/machine-learning Exerci