udacity machine learning course

algorithms include q-learning and time difference learning (temporal difference learning)In the case of enterprise Data application, the most commonly used is the model of supervised learning and unsupervised learning. In the field of image recognition, semi-supervised

to use this feedback data to help us improve our current application. If the system is based on machine learning, log data can be used to improve the performance of applications. Of course, user behavior data is often noisy. We need to consider how to remove noise and Improve the Quality of log data. In another example, you may also know Amazon Mechanical Turk.

-plane in a high-dimensional space separates the data points, which involves the mapping of non-linear data to high-dimensional to achieve the purpose of linear divisible data.Support Vector Concepts:The above sample map is a special two-dimensional situation, of course, the real situation may be many dimensions. Start with a simple understanding of what a support vector is at a low latitude. Can see 3 lines, the middle of the red line to the other tw

input data directly feedback to the model, the model must be immediately adjusted. Common application scenarios include dynamic systems and robot control. Common algorithms include q-learning and time difference learning (temporal difference learning)In the case of enterprise Data application, the most commonly used is the model of supervised

similarityAccording to the function and form similarity of the algorithm, we can classify the algorithm, for example, tree-based algorithm, neural network based algorithm and so on. Of course, the scope of machine learning is very large, and some algorithms are difficult to classify into a certain category. For some classifications, the same classification algor

Hello everyone, I am mac Jiang. See everyone's support for my blog, very touched. Today I am sharing my handwritten notes while learning the cornerstone of machine learning. When I was studying, I wrote down something that I thought was important, one for the sake of deepening the impression, and the other for the later review.Online

Course Description:This lesson focuses on the things you should be aware of in machine learning, including: Occam's Razor, sampling Bias, and Data snooping.Syllabus: 1, Occam ' s razor.2, sampling bias.3, Data snooping.1, Occam ' s Razor.Einstein once said a word: An explanation of the data should is made as simple as possible, but no simpler.There are similar s

The idea behind integrated learning is to combine different classifiers to get a meta-classifier, which has better generalization performance than a single classifier. For example, let's say we've got a forecast for an event from 10 experts, and integrated learning can combine these 10 predictions to get a more accurate forecast.We will learn later that there are different ways to create an integration mode

getting executed it have to is compiled (translated to CUDA or C). This makes debugging harder in Theano/tensorflow, since a error is much harder to associate with the line of code that CA Used it. Of course, doing things this is the have its advantages, but debugging isn ' t one of them.If you want-to-start out with Pytorch The official tutorials is very friendly to beginners but get-to-advanced topics as Well.First steps in

SVM is a widely used classifier, the full name of support vector machines , that is, SVM, in the absence of learning, my understanding of this classifier Chinese character is support/vector machines, after learning, Only to know that the original name is the support vector/machine, I understand this classifier is: by the sparse nature of a series of support vecto

of the most important aspects of machine learning is regularization and regularization, which will be detailed in subsequent chapters. Here is an intuitive understanding. The most common regularization item is the model of the constraint parameter. The following formula is used to constrain W: If y is a linear equation, the formula (1.4) is ridge regression. In Figure 1.7, we can see that the changed v

and regularization, which will be detailed in subsequent chapters. Here is an intuitive understanding. The most common regularization item is the model of the constraint parameter. The following formula is used to constrain W: If y is a linear equation, the formula (1.4) is ridge regression. In Figure 1.7, we can see that the changed value can have a huge impact on the model. When M = 9 is still used, it can be better fitted by adding it to the regularization item. Of

Today we share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-exercise solution for job three. I encountered a lot of difficulties in doing these topics, when I find the answer on the Internet but can not find, and Lin teacher does not provide answers, so I would like to do their own questions on how to think about the writing down,

(SVM) training algorithm can be classified into one of two categories after being entered into a new case, making itself a non-probabilistic binary linear classifier.The SVM model represents the training cases as points in space, which are mapped to a picture, separated by an explicit, widest possible interval to differentiate between two categories.Algorithm explanation: Support vector machine for machine