cs230 stanford

1. Find the costfunction to measure the error 2. Fit the theta parameter to minimize the costfunction. Uses gradient descent, iterates n times, iteratively updates Theta, and reduces costfunction 3. Find the appropriate parameter theta for

This paper uses the regularization linear regression model pre-flow (water flowing out of dam) according to the water storage line (water level) of the reservoir, then the Debug Learning Algorithm and discusses the influence of deviation and

This article covers the following topics: Single-Variable linear regression Cost function Gradient Descent Single-Variable linear regressionLooking back at the next section, in the regression problem, we have given the input

16.1 problem formalization16.2 Content-based recommender system16.3 Collaborative Filtering16.4 Collaborative filtering algorithm16.5 vectorization: Low-rank matrix decomposition16.6 Implementation of work Details: Normalization of the mean value

Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization,

19.1 Summary and acknowledgements Welcome to the last video on machine learning. We have been studying together for a long time. In the final video, I want to take a quick look at the main content of this course, and then briefly say a few words to

To draw a full stop to the first four sessions of the course, here are two of the models that were mentioned in the first four lectures by Andrew the Great God.The Perceptron Learning Algorithm Sensing machineModel:From the model, the Perceptron is

9. Clustering Content 9. Clustering 9.1 Supervised learning and unsupervised learning 9.2 K-means algorithm 9.3 Optimization Objective 9.4 Random Initialization 9.5 Choosing the number of Clusters 9.1 Supervised

The problem of regression is raised First, it needs to be clear that the fundamental purpose of the regression problem is prediction. For a problem, it is generally impossible to measure every situation (too much work), so we measure a set of

Supervised Learning Learn a function H: X → y H is called a hypothesis. 1. Linear Regression In this example, X is a two-dimensional vector, x1 represents living area, and x2 represents bedrooms. Functions/hypotheses H Set X0 = 1. Now,

23:55:01 | category: foreign university courses | Tag: machine learning | font size subscription INSTRUCTOR: Andrew Ng Http://see.stanford.edu/see/courseinfo.aspx? Coll = 348ca38a-3a6d-4052-937d-cb017338d7b1

SummaryThe main content of this lesson is about copying generic data. Although it is implemented in C language and does not use the generic programming technology such as template in C ++, the effect is very good. This section describes the

Continue to learn public lessonsThe second lesson does a simple calculator as an example. Probably touch the following knowledge points:Explaining the XCODE4, I looked at the latest download is XCode8.Xcode created the project, singleviewapplication

We will learn how to systematically improve machine learning algorithms, tell you when the algorithm is not doing well, and describe how to ' debug ' your learning algorithms and improve their performance "best practices". To optimize machine

If we are developing a machine learning system and want to try to improve the performance of a machine learning system, how do we decide which path we should choose Next?In order to explain this problem, to predict the price of learning examples. If

Drawing a learning curve is useful, for example, if you want to check your learning algorithm and run normally. Or you want to improve the performance or effect of the algorithm. Then the learning curve is a good tool. The learning curve can judge a

The use of neural networks to achieve autonomous driving, which means that the car through learning to drive themselves.It is a legend explaining how to realize automatic driving through neural network learning:The lower left corner is an image of

SVM is considered by many people to be the best algorithm for supervised learning, and I was trying to learn this time last year. However, the face of long formulas and the awkward Chinese translation eventually gave up. After a year, see Andrew to

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data 11.1 what to do firstIn the next video, I'll talk about the design of the machine learning

In general, the relationship between recall and precision is as follows:1, if the need for a high degree of confidence, the precision will be very high, the corresponding recall rate is very low, 2, if the need to avoid false negative, the recall