andrew ng coursera machine learning notes

-Gradient descentThe gradient descent algorithm is an algorithm for calculating the minimum value of a function, and here we will use the gradient descent algorithm to find the minimum value of the cost function.The idea of a gradient descent is that we randomly select a combination of parameters and calculate the cost function at the beginning, and then we look for the next combination of parameters that will reduce the value of the cost function.We continue this process until a local minimum (

-Gradient descent for linear regressionHere we apply the gradient descent algorithm to the linear regression model, we first review the gradient descent algorithm and the linear regression model:We then expand the slope of the gradient descent algorithm to the partial derivative:In most cases, the linear regression model cost function is shaped like a convex body, so the local minimum value is equivalent to the global minimum:The following is the entire convergence and parameter determination pr

 II. Linear Regression with one Variable (Week 1)-Model representationIn the case of previous predictions of house prices, let's say that our training set of regression questions (Training set) looks like this:We use the following notation to describe the amount of regression problems:-M represents the number of instances in the training set-X represents the feature/input variable-Y represents the target variable/output variable-(x, y) represents an instance of a training set-Representing the

it.The advantage of the online learning algorithm is that our algorithm can adapt well to the user's tendency, and the algorithm can update the model to adapt to the user's current behavior constantly.Map Reduce and data parallelismBatch gradient descent algorithm to solve the optimal solution of large-scale data sets = need to cycle the entire training set, calculate its partial derivative and cost, and then sum, the computational cost is very large

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

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

cost function least.The algorithm is:After derivation, get:Note: Although the resulting gradient descent algorithm appears to be the same as the gradient descent algorithm for linear regression, the hypothetical function here differs from the linear regression, so it is actually different. In addition, it is still necessary to perform feature scaling before applying the gradient descent algorithm.In addition, there are some alternatives to the gradient descent algorithm:In addition to the gradi

-Polynomial regressionSince linear regression does not apply to all data, sometimes we need to use curves to fit our data, for example, with two-times polynomial:Or three-time polynomial:Usually we need to look at the data before deciding what model to try to fit.After that, we can make:The two-time polynomial is then converted to a linear regression model.It is worth noting that if we adopt a polynomial regression model, feature scaling is necessary before the gradient descent algorithm is run.

the transpose of the Matrix.-Gradient descent for multiple variablesSimilar to univariate/feature linear regression, in multivariable/feature linear regression, we will also define a cost function, namely:Our goal is the same as the problem in univariate/characteristic linear regression, which is to find out the combination of parameters that make the cost function least.Therefore, the multivariable/linear regression gradient descent algorithm is:ThatAfter the derivative number can be obtained:

NG Machine Learning Video notes (11)--k - means algorithm theory(Reproduced please attach this article link--linhxx)I. OverviewK-Means (K-means) algorithm, is a unsupervised learning (unsupervised learning) algorithm, its core is

and the computational optimization of the problem is discussed.Collaborativefiltering algorithm:We can iteratively optimize the theta and eigenvectors, but this performance is relatively low, so now consider improving the performance of the algorithm. At the same time, two kinds of methods are solved.is to combine the two method optimization functions to get the overall objective function.Algorithm Flowchart:Exercises:Vectorization Low Rank matrix factorization:The main thing here is to constru

When should do we use fine-tuning?It is typically used only if you have a large labeled training set; In this setting, fine-tuning can significantly improve the performance of your classifier. However, if you had a large unlabeled dataset (for unsupervised feature learning/pre-training) and only a relatively smal L labeled training Set, then fine-tuning was significantly less likely to help.Stacked Autoencoders (Training):Equivalent to capturing the c

NG Machine Learning Video notes (ii)--Gradient descent algorithm interpretation and solving θ (Reproduced please attach this article link--linhxx) First, the interpretation gradient algorithmA gradient algorithm formula and a simplified cost function diagram, as shown in.1) Partial derivativeBy the know, at point A, it

Programmers who have turned to AI have followed this number ☝☝☝ Author: Lisa Song Microsoft Headquarters Cloud Intelligence Advanced data scientist, now lives in Seattle. With years of experience in machine learning and deep learning, we are familiar with the requirements analysis, architecture design, algorithmic development and integrated deployment of

Coursera Andrew Ng Machine learning is really too hot, recently had time to spend 20 days (3 hours a day or so) finally finished learning all the courses, summarized as follows:(1) Suitable for getting started, speaking the compar

diagnosis of benign or malignant tumors (this is a supervised learning problem), your decision gives a conclusion that determines the life and death of a patient. However, you might actually need to make multiple decisions in a row over time. For example, an unmanned helicopter's automatic flight, you make a wrong decision, he may not crash immediately, as long as you make the right decision, can be remedied, only if you have been making the wrong de

What are machine learning?The definitions of machine learning is offered. Arthur Samuel described it as: "The field of study that gives computers the ability to learn without being explicitly prog Rammed. " This was an older, informal definition.Tom Mitchell provides a more modern definition: 'a computer program was sa

regression. The root number can also be selected based on the actual situation.Regular Equation In addition to Iteration Methods, linear algebra can be used to directly calculate $ \ matrix {\ Theta} $. For example, four groups of property price forecasts: Least Squares $ \ Theta = (\ matrix {x} ^ t \ matrix {x}) ^ {-1} \ matrix {x} ^ t \ matrix {y} $Gradient Descent, advantages and disadvantages of regular equations Gradient Descent: Desired stride $ \ Alpha $; Multiple iterations are requ