advanced machine learning coursera

3. Bayesian Statistics and regularizationContent3. Bayesian statistics and regularization.3.1 Underfitting and overfitting.3.2 Bayesian statistics and regularization.3.3 Optimize cost function by regularization.3.3.1 regularized linear regression.3.3.2 regularized Logistic regression.3.4 Advanced Optimization.3.1 underfitting and overfittingThe linear regression model and logistic regression model have been studied before, and they have been applied i

did you not mention? A: The JVM is an unusually complex thing, writing a brick book is not too much, there are a few to explain:Chang (Constant pool): The area in which the constants in the program are stored sequentially and indexed. For example, int i = 100, this 100 is placed in the constant pool.SecurityManager: Provides security controls for Java runtime to prevent malicious attacks, such as specifying read files, write file permissions, network access, create processes, and so on, Class l

a development set (validation set)Validate cross-validation with a model verification methodLeave a validation (for early)A certain percentage of random sampling is used as a training set, leaving the usual proportion of 7 3 as a validation set, but the performance of the model is unstable due to the uncertainty of the validation set random samplingCross-validation (leave an advanced version of authentication)The average result is obtained after leav

another feature of the library Numarray of the same nature, and added other extensions and developed the NumPy. NumPy is open source and co-maintained by many collaborators to develop.2 Matplotlib Brief IntroductionMatplotlib is a library of very similar MATLAB environments that generate publishing quality data. The user can output the data in a pop-up window as a raster format (PNG, TIFF, JPG) or as a vector file (e.g. EPS, PS). Matlab users are familiar with the graphics types and syntax for

machine learning algorithms.In this section, we mainly introduce the use of naive Bayesian method for the classification of text, we will use a set of tagged categories of text documents to train naive Bayesian classifier, and then to the unknown data instances of the category prediction. This method can be used as a filter for spam messages.Data setThe data of this experiment can get a set of news informa

SAS graphical user interfaces help you build machine-learning models and implement an iterative machine learning process. You don ' t have a advanced statistician. Our comprehensive selection of the machine

Tags: 9.png update regular des mini RAC spam ORM ProofOrganize the machine learning course from Adrew Ng week3Directory: Two classification problems Model representation Decision Boundary Loss function Multi-Classification problem Over-fitting problems and regularization What is overfitting How to resolve a fit Regularization method

Tools used: NumPy and MatplotlibNumPy is the most basic Python programming library in the book. In addition to providing some advanced mathematical algorithms, it also has a very efficient vector and matrix operations function. These are particularly important for computational tasks for machine learning. Because both the characteristics of the data, or the batch

Machine learning methods can be divided into generative and discriminative methods. Generative model: assume that the input is X and the category label is Y. The generative model estimates the joint probability P (X, Y) Because samples can be generated based on the joint probability. Discriminative model: assume that the input is X and the category label is Y. The discriminant model estimates the conditiona

according to this parameter estimation and the sample calculation category distribution Q.3 The extremum of the nether function is obtained, and the parameter distribution is updated.4 iterations are calculated until convergence.Say Ah, the EM algorithm is said to be a machine learning advanced algorithm, but at least for the moment, it is still easy to understa

: One-to-multiple ) Sometimes the problem is not as simple as determining whether a patient's tumor is malignant or benign. For example, determining whether the weather is sunny, cloudy, raining, Or snowing is necessary. We can use a line to separate binary classification. What about multiclass classification? There is a simple method, that is, to separate only one category at a time. There are several categories to construct several decision edge, that is, severalH (x): In th

First, cross-validation.Cross-validation (validation) is an evaluation of statistical analysis, machine learning algorithms for data sets independent of the training data generalization ability (generalize), can avoid overfitting problems.Cross-validation generally needs to be as satisfying as possible:1) The proportion of the training set should be enough, generally more than half2) uniform sampling of tra

Omit the use of octave end, later use to see itWeek Three:Logistic Regression:For 0-1 categoriesHypothesis representation:: Sigmoid function or Logistic functionDecision Boundary:Theta's Transpose * small x>=0 is boundaryMay:non-linear decision boundaries, constructing the polynomial of XCost function:Simplified cost function and gradient descent:Because Y has only two values, merging:To find the least biased guide:(The denominator should be ignored)Advanced