andrew ng coursera machine learning notes

for linear regressionWe take the formula of the cost function J into the gradient descent algorithm, then use the concept of partial derivative to simplify the formula, and finally we can get the formula. The specific derivation requires some knowledge of calculus.We can actually use them directly. That is, the algorithm is probably written like this, we use these two formulas to constantly revise the value of two parameters, until the function J reached a minimum value. Now that we have this f

updated, and a final θj value is obtained.The entire derivative is calculated as follows:Vector representation of ④ hypothesis function, cost function and gradient descent algorithmSuppose the vector of the function is represented as follows:The cost function is represented as follows:The vectorization of θ using the gradient descent algorithm is represented as follows:(There is an error in the original formula, the formula after the first equals should not be divided by M, corrected here)The c

This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This cou

This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This cou

what is linear regression. The so-called linear regression (taking a single variable as an example) is to give you a bunch of points, and you need to find a straight line from this pile of points. Figure below This screenshot is from Andrew Ng's What you can do when you find this line. Let's say we find A and b that represent the line, then the line expression is y = a + b*x, so when a new x is present, we can know Y.

it is easy to cause the overflow. This is because X and ln (x) have the same monotonicity, and both sides take the logarithmSo this is the J (Theta) that Andrew gave, and the only difference is that Andrew has a negative coefficient in front of it, which makes the maximum value a minimum, so that the gradient descent algorithm can be used.But in fact, with this formula can also complete the task, just use

rate to characterize the model.mly--12. Takeaways:setting up development and test sets1. Your validation set and test set should be captured as much as possible from the data in your actual application scenario. Validation sets and test sets do not have to be distributed identically to your training data. (I think it's best to have a similar distribution between the training set and the validation set, if the training data and the validation data are distributed too much, you may be able to tra

the gradient descent, when we calculate the derivative term, we need to do the summation, so, in each individual gradient descent, we finally have to calculate such a thing, this item needs to sum all the m training samples. In the following lesson, we will also talk about a method that can solve the minimum value of the cost function J without the need for multi-step gradient descent, which is another called normal equation (normal equations) . The method. In fact, the gradient descent method

of the weights is (0,1).The main ideas of local weighted linear regression are:Where weights are assumed to conform to the formulaThe weight size in the formula depends on the distance between the predicted point X and the training sample. If |-x| is smaller, then the value is close to 1, and vice versa is close to 0. The parameters tau, called bandwidth, are used to control the amplitude of the weights.The advantage of local weighted linear regression is that it is less dependent on feature se

"linear regression, gradient descent" The regular equationThe training features are represented as X-matrices, the results are expressed as Y-vectors, and the linear regression model is still the same, and the loss function is unchanged. Then θ can be derived directly from the following formula: The derivation process involves the knowledge of linear algebra, where the linear algebra knowledge is not expanded in detail. Set m as the number of training samples; x is the independent variable in

method provides a method for finding the θ value of the f (θ) =0. How to maximize the likelihood function ? What is the maximum value of the first derivative at the corresponding point? (θ) to zero. So let f (θ) =? ' (θ), maximized ? (θ) can be converted to: Newton's method of seeking ? (θ) The problem of =0 Theta . The expression of the Newton method, the iterative update formula forθ is:Newton-Slavic iteration (Newton-raphson method)in the logistic regression, θ is a vector, so we generalize

build the model.In the exponential distribution family expression of the Bernoulli distribution we have known:, thus obtained.Three assumptions for building a generalized linear model: Assuming that the Bernoulli distribution is met, , in Bernoulli distribution The derivation process is as follows:As with the least squares model, the next work is done by gradient descent or Newton's method.Note the above push to the result, recall, in the logistic regression, we choose th

This paper mainly records the cost function of neural network, the usage of gradient descent in neural network, the reverse propagation, the gradient test, the stochastic initialization and other theories, and attaches the MATLAB code and comments of the relevant parts of the course work. Concepts of neural networks, models, and calculation of predictive classification using forward propagation refer to Andrew Ng