anaconda machine learning

Machine Learning Summary (1), machine learning SummaryIntelligence:The word "intelligence" can be defined in many ways. Here we define it as being able to make the right decision based on certain situations. Knowledge is required to make a good decision, and this knowledge must be operable, for example, interpreting se

Https://github.com/josephmisiti/awesome-machine-learning#julia-nlp Julia General-purpose Machine Learning Machinelearning-julia Machine Learning LibraryMlbase-a set of functions to support development of

Simple examples are used to understand what machine learning is, and examples are used to understand machine learning. 1. What is machine learning? What is machine

Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduc

Bayesian Introduction Bayesian learning Method characteristic Bayes rule maximum hypothesis example basic probability formula table Machine learning learning speed is not fast enough, but hope to learn more down-to-earth. After all, although it is it but more biased in mathematics, so to learn the rigorous and thoroug

Original handout of Stanford Machine Learning Course This resource is the original handout of the Stanford machine learning course, which is AndrewNg said that a total of 20 PDF files cover some important models, algorithms, and concepts in machine

This article focuses on the contents of the 1.2Python libraries and functions in the first chapter of the Python machine learning time Guide. Learn the workflow of machine Learning.I. Acquisition and inspection of dataRequests getting dataPandans processing Data1 ImportOS2 ImportPandas as PD3 ImportRequests4 5PATH = R'E:/python

in the process of learning rate can be seen as the length of the descent process, assuming that your step is very big can cross the valley directly on the opposite side of the mountain, it is difficult to get the local optimal solution. At this point, reducing the step size will increase your chances of going to the ground.2. About the cross fittingBy using the methods of drop out, batch normalization and data argument, the generalization ability of

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 has been told, it is difficult to understand ho

some time ago on the Internet to see the Coursera Open Classroom Big Machine learning Cornerstone Course, more comprehensive and clear machine learning needs of the basic knowledge, theoretical basis to explain. There are several more important concepts and ideas in foundation, first review, and then open the follow-up

to the right in this image. We can generally see the two learning curves, the two curves of blue and red are approaching each other. Therefore, if we extend the curve to the right, it seems that the training set error is likely to increase gradually. The cross-validation set error will continue to decline. Of course, we are most concerned with cross-validation set errors or test set errors. So from this picture, we can basically predict that if we co

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

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) Build your own, new, and better features. A litt

July online April machine learning algorithm class notes--no.1 Objective Machine learning is a multidisciplinary interdisciplinary, including probability theory, statistics, convex analysis, feature engineering and so on. Recently followed the July algorithm to learn the knowledge of

Machine Learning 4, machine learning Probability-based classification method: Naive BayesBayesian decision theory Naive Bayes is a part of Bayesian decision-making theory. Therefore, before explaining Naive Bayes, let's take a quick look at Bayesian decision-making theory knowledge. The core idea of Bayesian decision-m

In machine learning, often need to calculate the distance between each sample, used for classification, according to distance, different samples grouped into a class; But in the current machine learning algorithm, the distance calculation mode is endless, then this blog is mainly to comb the current

A brief introduction to Learning _note1 against Sample machine Machine learning methods, such as SVM, neural network, etc., although in the problem such as image classification has been outperform the ability of human beings to deal with similar problems, but also has its inherent defects, that our training sets are fe