stanford university machine learning coursera

the value is, the closer the value of the evaluation function is to the midline position of the parabolic curve, that is, the closer it is to the minimum value. It can be represented by an example: Let's take a look at the meaning. When the value is too small, the update is slow, and the gradient descent algorithm will slow down in execution. When the value is too large, the gradient descent algorithm may exceed the target value (minimum value), leading to non-convergence, even divergence. As

On Github, Afshinea contributed a memo to the classic Stanford CS229 Course, which included supervised learning, unsupervised learning, and knowledge of probability and statistics, linear algebra, and calculus for further studies. Project Address: https://github.com/afshinea/stanford-cs-229-

. Optimal interval classifierThe optimal interval classifier can be regarded as the predecessor of the support vector machine, and is a learning algorithm, which chooses the specific W and b to maximize the geometrical interval. The optimal classification interval is an optimization problem such as the following:That is, select Γ,w,b to maximize gamma, while satisfying the condition: the maximum geometry in

Extremely light of a semester finally passed, summer vacation intends to learn the big step down this machine learning techniques.The first lesson is the introduction of SVM, although I have learned it before, but I heard a feeling is very rewarding. The blogger sums up a ballpark figure, and the specifics areTo listen: http://www.cnblogs.com/bourneli/p/4198839.htmlThe blogger sums it up in detail: http://w

)/∂ (θ (1) JK) is tested for gradients. After the partial derivative code does not have a problem, close the Gradient check section code.6. Use gradient descent or other advanced algorithms to perform reverse propagation to find the θ values for minimizing j (θ).This paper describes the gradient descent algorithm in neural networks: starting from the random initial point, descending step by step, until the local optimal value is obtained. Algorithms such as gradient descent can at least guarante

dimension.Finally, we propose a method for solving overfitting, including data cleaning/pruning, data hinting, regularization (regularization), confirmation (validation), andTo drive for example to illustrate the role of these methods, the latter two methods are also the contents of the following two lessons.Data cleaning/pruning is to correct or delete the wrong sample points, processing is simple, but usually such sample points are not easy to find.Data hinting generate more sample numbers by

-Feature ScalingWhen we are faced with multidimensional feature problems, we need to ensure that the multidimensional features have similar scales, which will help the gradient descent algorithm to converge faster.Take the housing price forecast problem as an example, assuming that the two characteristics we use, namely the size of the house and the number of rooms, the size value range is 0-2000 square feet, and the value of the room number is 0-5, which causes the gradient descent algorithm to

-Cost functionFor the training set and our assumptions, we will consider how to determine the coefficients in the assumptions.What we are going to do now is to choose the right parameters, and the selection of parameters directly affects the accuracy of the resulting straight line for the training set description. The difference between the predicted value and the actual value in the training set is the modeling error (Modeling error).the cost function is defined by calculating the sum of square

than or equal to 0, which is greater than or equal to 3 o'clock, the model predicts y = 1.We can draw a straight line, which is the dividing line of our model, separating the area predicted to 1 and the area predicted as 0.What kind of model would be appropriate if our data were to be presented in the following circumstances?Because curves are required to separate areas of y = 0 and y = 1, we need two-character:Assuming that the parameter is [-1 0 0 1 1], then we get the decision boundary is ex

two classification problem, so the model is modeled as Bernoulli distributionIn the case of a given Y, naive Bayes assumes that each word appears to be independent of each other, and that each word appears to be a two classification problem, that is, it is also modeled as a Bernoulli distribution.In the GDA model, it is assumed that we are still dealing with a two classification problem, and that the models are still modeled as Bernoulli distributions.In the case of a given y, the value of x is

before, but you need to define T (Y) here:In addition, make:(t (y)) I represents the first element of the vector T (y), such as: (t (1)) 1=1 (T (1)) 2=01{.} is an indicator function, 1{true} = 1, 1{false} = 0(T (y)) i = 1{y = i}Thus, we can introduce the multivariate distribution of the exponential distribution family form:1.2 The goal is to predict the expectation of T (y), because T (y) is a vector, so the resulting output will also be a desired vector, where each element is:Corresponds to th

be trained and predicted immediately, which is called Online learning. each of the previously learned models can do online learning, but given the real-time nature, not every model can be updated in a short time and the next prediction, and the perceptron algorithm is well suited to do online learning:The parameter Update method is: if hθ (x) = y is accurate, the parameter is not updated otherwise, θ:=θ+ y

hyper-plane (w,b) and the entire training set is defined as:Similar to the function interval, take the smallest geometric interval in the sample.The maximum interval classifier can be regarded as the predecessor of the support vector machine, and is a learning algorithm, which chooses the specific W and b to maximize the geometrical interval. The maximum classification interval is an optimization problem s

Mainly for the week content: large-scale machine learning, cases, summary(i) Random gradient descent methodIf there is a large-scale training set, the normal batch gradient descent method needs to calculate the sum of squares of errors across the entire training set, which is a very large computational cost if the learning method needs to iterate 20 times.First,

Mainly for the sixth week Content machine learning application recommendations and system design.What to do nextWhen training good one model, predicting unknown data discovery, how to improve it? Get more examples of training Try to reduce the number of features Try to get more features Try adding two-item features Try to reduce the degree of normalization λ Try to increase the

-Unsupervised learningIn supervised learning, whether it is a regression problem or a classification problem, we use the data to have a clear label or the corresponding prediction results.In unsupervised learning, our existing data have no corresponding results or labels, and some are just features. Therefore, the problem to be solved by unsupervised learning is

would the Vectorize this code to run without all for loops? Check all the Apply. A: v = A * x; B: v = Ax; C: V =x ' * A; D: v = SUM (A * x); Answer: A. v = a * x; v = ax:undefined function or variable ' Ax '. 4.Say you has a vectors v and Wwith 7 elements (i.e., they has dimensions 7x1). Consider the following code: z = 0; For i = 1:7 Z = z + V (i) * W (i) End Which of the following vectorizations correctly compute Z? Check all the Apply.

I've been talking about why machines can learn, and starting with this lesson are some basic machine learning algorithms, i.e. how machines learn.This lesson is about linear regression, starting with the minimization of Ein, introducing the Hat Matrix to understand the geometric meaning. Finally, the linear regression and binary classification are compared, and the reason why linear regression can be used t

Mainly for the ninth week content: Anomaly detection, recommendation system(i) Anomaly detection (DENSITY estimation) kernel density estimation ( Kernel density estimation X (1) , X (2) ,.., x (m) If the data set is normal, we want to know the new data X (test) p (x) After density estimation, it is a common method to select a probability threshold to determine whether it is an anomaly, which is often used in anomaly detection. Such as: Gaussian distributionThe Gaussian k

a patient's tumour is malignant, depending on the size of the patient's tumour:Of course, sometimes we use more than one variable, such as the age of the patient, the size and shape of the tumour, and so on.In the picture, the circle represents benign and the fork is malignant, and the problem we want to learn becomes the division of benign tumors and malignant tumors.This problem is also called classification problem, the classification of the use of discrete values. We want to use this algori