nyu machine learning course

, as shown in: Step 4: run the model. After completing the preceding operations, you can run the program. Click "run" at the bottom to run the model. After each module is run, a green check box is displayed in the upper right corner, if an error occurs in each module or step, a red icon will appear in the same place. After you move the mouse over it, an error type will be displayed. Step 5: view the result. Right-click the dot in the "Evaluate Model" box and select "Visualize" to view the mode

Machine learning is a comprehensive and applied discipline that can be used to solve problems in various fields such as computer vision/biology/robotics and everyday languages, as a result of research on artificial intelligence, and machine learning is designed to enable computers to have the ability to learn as humans

As an article of the College (http://xxwenda.com/article/584), the follow-up preparation is to be tested individually. Of course, there have been many tests. Apache Spark itself1.MLlibAmplabSpark was originally born in the Berkeley Amplab Laboratory and is still a Amplab project, though not in the Apache Spark Foundation, but still has a considerable place in your daily GitHub program.ML BaseThe mllib of the spark itself is at the bottom of the three

clearly explained. It also covers De Rham cohomology and Lie algebra, where audience is invited to discover the beauty by linking geometry wi Th algebra.Modern Graph theoryBela BollobasIt is a modern treatment of this classical theory, which emphasizes the connections and other mathematical subjects--fo R example, random walks and electrical networks. I found some messages conveyed by the This book was enlightening for my all in machine

Original address: http://www.cnblogs.com/cyruszhu/p/5496913.htmlDo not use for commercial use without permission! For related requests, please contact the author: [Email protected]Reproduced please attach the original link, thank you.1 BasicsL Andrew NG's machine learning video.Connection: homepage, material.L 2.2008-year Andrew Ng CS229 machine LearningOf

1.1 machine learning basics-python deep machine learning, 1.1-python Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang Video tutorial: http://pan.baidu.com/s/1kVNe5EJ 1. course Introduction 2.

corresponding to the numerical solution. Therefore, this solution is not the smallest solution that is solved step by step, as mentioned earlier by the PLA algorithm.Answer is the reason for more emphasis on the results, the direct solution is the mathematical derivation of the exact solution, so that the minimum solution is obtained, in line with the solution conditions, but also to solve the pseudo-inverse algorithm (this method is called Gaussian elimination method, see also Gauss, looked at

Application Recommendations for machine learningFor a long time, the machine learning notes have not been updated, the last part of the updated neural network. This time we'll talk about the application of machine learning recommendations.Decide what to do nextSuppose we nee

Original address: http://blog.csdn.net/lrs1353281004/article/details/79529818 Sorting out the machine learning-algorithm engineers need to master the basic knowledge of machine learning, and attached to the internet I think that write a better blog address for reference. (Continuous update)

intervention on the results of model training it's a lever. Model does not understand the business, really understand the business is people. What the model can do is to learn from the cost function and sample, and find the optimal fit of the current sample. Therefore, machine learning workers should be appropriate to the needs of the characteristics of some human intervention and "guidance", such as the h

Recently saw a relatively good machine learning course, roughly heard it again. The overall sense of machine learning field is still more difficult, although Li Hongyi teacher said is very good, not enough to absorb up or have a certain difficulty. Even though the process ha

first, gradient descent method In the machine learning algorithm, for many supervised learning models, the loss function of the original model needs to be constructed, then the loss function is optimized by the optimization algorithm in order to find the optimal parameter. In the optimization algorithm of machine

Reprint Please specify the Source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectoryMachine learning Cornerstone Note When machine learning can be used (1)Machine learning Cornerstone Note 2--When you can use machine

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

) Discriminant analysis is mainly in the statistics over there, so I am not very familiar with the temporary find statistics Department of the Boudoir Honey made up a missed lesson. Here we are now learning to sell. A typical example of discriminant analysis is linear discriminant analysis (Linear discriminant analyses), referred to as LDA. (notice here not to be confused with the implied Dirichlet distribution (latent Dirichlet allocation), although

Python Machine Learning Theory and Practice (6) Support Vector Machine and python Learning Theory In the previous section, the theory of SVM is basically pushed down, and the goal of finding the maximum interval is finally converted to the problem of solving the alpha of the Child variable of the Laplace multiplication

Knowing an algorithm and using an algorithm are two different things. What should I do if I find that the model has a big error after you train the data? 1) Obtain more data. It may be useful. 2) reduce feature dimensions. You can manually select one or use mathematical methods such as PCA. 3) Obtain more features. Of course, this method is time-consuming and not necessarily useful. 4) add polynomial features. Are you trying to save your life? 5) Bui

First, the foregoingNumPy(numerical python abbreviation) is an open source Python Scientific Computing Library. Use NumPy , you can use arrays and matrices in a very natural way . Numpy contains many useful mathematical functions, including linear algebra operations, Fourier transforms, and random number generation functions . The Library's predecessor was a library for array operations that began in 1995 years. After a long period of development, it has basically become the most basic Python

of the current node is the middle half of the distance of all its leaf nodes is float (NUMLEAFS)/2.0/plottree.totalw* 1, but since the start Plottree.xoff assignment is not starting from 0, but the left half of the table, so also need to add half the table distance is 1/2/plottree.totalw*1, then add up is (1.0 + float (numleafs))/2.0/ Plottree.totalw*1, so the offset is determined, then the X position becomes Plottree.xoff + (1.0 + float (numleafs))/2.0/PLOTTREE.TOTALW3, for Plottree function p

Gradient descent algorithm minimization of cost function J gradient descent Using the whole machine learning minimization first look at the General J () function problem We have J (θ0,θ1) we want to get min J (θ0,θ1) gradient drop for more general functions J (Θ0,θ1,θ2 .....) θn) min J (θ0,θ1,θ2 .....) Θn) How this algorithm works. : Starting from the initial assumption Starting from 0, 0 (or any other valu