) ^2\)To break it apart, it was \ (\frac1 2 \bar{x}\) where \ (\bar{x}\) is the mean of the squares of $h _θ (x_i)? Y_i $, or the difference between the predicted value and the actual value.This function is otherwise called the "Squared error function", or "Mean squared error". The mean is halved \ ((\frac1 2) \) as a convenience for the computation of the gradient descent, as the derivative Term of the square function would cancel out the \frac1 2\ . The following image summarizes what is the c
The process of building a machine learning algorithm:
Quickly build a simple algorithm and test the performance of the algorithm with a cross-validation set.
Draw the learning curve, check whether the algorithm has high variance or high deviation problem, so as to choose corresponding coping methods.
Error analysis, to see the
In both industry and academia, machine learning is a hot direction, but academia and industry focus on machine learning, academia focuses on the study of machine learning theory, and industry focuses on how to solve practical prob
examples.
Algorithms of the Intelligent Web (Smart Web algorithm) PDFAuthor Haralambos Marmanis, Dmitry Babenko. The formula in this book is a little bit more than "collective intelligence programming", the example of which is mostly the application on the Internet, to see the name. The disadvantage is that the matching code inside is BeanShell and not python or anything else. In general, this book is still suitable for beginners, and the same need
From Cold War to deep learning: An Illustrated History of machine translationSelected from vas3k.comIlya PestovEnglish Translator: Vasily ZubarevChinese Translator: Panda
The dream of high quality machine translation has been around for many years and many scientists have contributed their time and effort to this dream. From early rule-based
Machine Learning (machines learning, ML) is a multidisciplinary interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithmic complexity theory and many other disciplines. Specialized in computer simulation or realization of human learning behavior, in order to a
. 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
statistical tests for each feature:false positive rate SELECTFPR, false discovery rate selectfdr, or family wise error selectfwe. The document says that if you use a sparse matrix, only the CHI2 indicator is available, and everything else must be transformed into the dense matrix. But I actually found that f_classif can also be used in sparse matrices.Recursive Feature elimination: Looping feature selectionInstead of examining the value of a variable individually, it aggregates it together for
Machine Learning and Its Application in Information Retrieval
-- Notes about researcher Li Hang
12Month28No. We have ushered in a new "cutting-edge research lecture". The speaker of this lecture is Li Hang Doctor. Instructor Li is currently at the Microsoft Asia Research Institute. Information Retrieval and Mining Group ( Rem ) Senior Researcher, Rem Its main mission is to develop more advanced s
extract high-quality features. Iii. What is the difference between machine learning and deep learning?1. IntroductionCalled Deep learning So there must be a relative shallow learning(machine
start.Getting Started with machine learningA very introductory lecture that introduces the basic concepts of machine learning, such as what is a model, and the basic steps of machine learning: setting goals and benchmarking criteria, collecting and cleaning data, exploring
Machine learning is undoubtedly an important content in the field of data analysis now, people who engage in it work are in the usual work or manyor less will use machine learning algorithms.There are many algorithms for machine learning
teaches itself something". Rosenblatt, known as the Perceptron, can learn to classify simple images, such as triangles and squares. Rosenblatt usually realizes his ideas in the giant machine that wraps the thread, but they build the basic principles of today's artificial neural network.One computer he built had eight simulated neurons, made from motors and dials connected to the light detectors. Each of the neurons received a share of the signals fro
disease progression or the iris job data set for pattern recognition can explain how machine learning algorithms work in Scikit . Furthermore, the library provides information on loading datasets from external sources, including sample generators for tasks, such as multi-class classification and decomposition, as well as recommendations for the use of popular data sets. 4.2 Audience and
straight line, but it does not need to be guaranteed.That is, to tolerate those error points, but we have to add the penalty function so that the more reasonable the error points, the better. In fact, in many cases, the more perfect the classification function is not during training, the better, because some data in the training function is inherently noisy. It may be wrong when the classification label is manually added, if we have learned these error points during training (
into Java bytecode. The main purpose is to make full use of procedural languages to build programs with rich granularity and free structure, so as to meet the special needs brought about by various problems. Here are two examples of the advantages of the Procedural language they use: the first one isThey use the image library almost everywhere.. For each user, their graphic computing can be called a low-latency, streamlined map-Reduce task set. Anoth
and negative examples can be correctly separated by a super-surface in Rn, the problem is called nonlinear separable problem.The solution method: Nonlinear transformation, the nonlinear problem into a linear problem.The basic idea of applying nuclear techniques to support vector machines is to use a nonlinear transformation to correspond the input space (Euclidean space Rn or discrete set) to a feature space (Hilbert space H), so that the hyper-surfa
accuracy accuracy rate to measure the performance of the algorithm. Typically, we set up a test set to test network performance. The test set does not intersect with the training set, the validation set (the training set contains the data for training learning, the validation set is used to select the optimal parameters, etc.). Many times we will find that performance is a difficult problem to quantify. In supervised
products, and so on, can be abstracted into vectors to allow the computer to know the distance between two properties. For example: We believe that 18-year-olds are closer to the 24-year-old than the 12-year-old, which is closer to the product than the computer, and so on.as long as the real-world objects can be abstracted into vectors, you can use the K-means algorithm to classify .In the "K-mean Clustering (K-means)" This article cited a very good application example, the author made a vector
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