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", a book written by Chinese scientists, is quite understandable.6. "Managing gigabytes", a good book of information retrieval.7. "Information theory:inference and Learning Algorithms", reference books, relatively deep.Relevant mathematical basis (reference books, not suitable to read through):1. Linear algebra: This r
What is integrated learning, in a word, heads the top of Zhuge Liang. In the performance of classification, multiple weak classifier combinations become strong classifiers. In a word, it is assumed that there are some differences between the weak classifiers (such as different algorithms, or different parameters of the same algorithm), which results in different classification decision boundaries, which me
We will learn how to systematically improve machine learning algorithms, tell you when the algorithm is not doing well, and describe how to ' debug ' your learning algorithms and improve their performance "best practices". To optimize ma
say. However, two books are recommended for those who have just contacted NLTK or need to know more about NLTK: One is the official "Natural Language processing with Python" to introduce the function usage in NLTK, with some Python knowledge, At the same time the domestic Chen Tao classmate Friendship translated a Chinese version, here you can see: recommended "natural language processing with Python" Chinese translation-nltk supporting book; another
Source: https://www.cnblogs.com/jianxinzhou/p/4083921.html1. The problem of overfitting (1) Let's look at the example of predicting house price. We will first perform linear regression on the data, that is, the first graph on the left. If we do this, we can obtain such a straight line that fits the data, but in fact this is not a good model. Let's look at the data. Obviously, as the area of the house increases, the changes in the housing price tend to be stable, or the more you move to the right
The motive and application of machine learningTools: Need genuine: Matlab, free: Octavedefinition (Arthur Samuel 1959):The research field that gives the computer learning ability without directly programming the problem.Example: Arthur's chess procedure, calculates the probability of winning each step, and eventually defeats the program author himself. (Feel the idea of using decision trees)definition 2(Tom
will find nothing to say, just give a lot of examples. Algorithms of the Intelligent Web (Smart Web algorithm) PDF138Author 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 pyth
clusters. Clustering is when you don't know exactly how many classes the target database has, and you want to make all the records into different classes or clusters, and in this case, The similarity of a metric (for example, distance) is minimized between the same cluster and maximized among different clustering classes. Unlike classification, unsupervised learning does not rely on a predefined class or band-mark training instance, which needs to be
Statement: This blog post according to Http://www.ctocio.com/hotnews/15919.html collation, the original author Zhang Meng, respect for the original.Machine learning is undoubtedly a hot topic in the field of current data analysis. Many people use machine learning algorithms more or less in their usual work. This arti
deep understanding of machine learning: Learning Notes from principles to algorithms-1th week 02 easy to get started Deep understanding of machine learning from principle to algorithmic learn
: Network Disk DownloadContent IntroductionThis book is a practical reference for robotics and machine vision, the first part of the "Basics" (chapters 2nd and 3rd) describes the position and posture of the robot and its operating objects, as well as the representation of the path and motion of the robot; Part II "Mobile robot" (Chapters 4th to 6th) introduce their basic motion control modes and their navig
training process, because most of the machine learning algorithms are not obtained by the Analytic method, but are slowly optimized by iterative iteration. So cross-validation data can be used to monitor the performance changes during model training. Test data: After the model has been trained, the test data is used to measure the performance of the final model,
, so as to better identify the problem and adjust the model. The most noteworthy is the feature engineering , the characteristics of the design is often more like an art. In general or to accumulate more, more divergent thinking, hands-on to do, reflect on the summary, gradual.Review of each chapterGetting Started with 1.Python machine learning: This paper introduces the orientation of the
Use Python to master machine learning in four steps and python to master machines in four steps To understand and apply machine learning technology, you need to learn Python or R. Both are programming languages similar to C, Java, and PHP. However, since Python and R are both relatively young and "Far Away" from the CP
contrary to our original intention. Look at the judging criteria below. Use p to denote precision,r expression recall;If we choose the judging standard = (p+r)/2, then algorithm3 wins, obviously unreasonable. Here we introduce an evaluation criterion: F1-score.When P=0 or r=0, there is f=0;When P=1r=1, there is f=1, maximum;Also we apply F1 score to the above three algorithms, the result is algorithm1 maximum, which is the best; algorithm3 is the sma
Recently intermittent toss on VMware installed 64-bit Ubuntu virtual system on the learning of Linux and write simple code, also familiar with the image recognition program. Experienced the following process, is now recorded for future reference.1. Own two notebooks, a Dell ultra-polar Ben 6430U, SSD hard disk 128G, performance can also be a bit small hard disk, Thinkpad T410, hard disk, too heavy. Super-great Ben is really light. Tangled or want to c
learning Adventure JourneysklearnProvides a lot of machine learning algorithm implementation, in the learning process I can not do a full study and coverage. After many searches, I found the Youtube sentdex released video "machine Learn
For the performance of four different algorithms in different size data, it can be seen that with the increase of data volume, the performance of the algorithm tends to be close. That is, no matter how bad the algorithm, the amount of data is very large, the algorithm can perform well.When the amount of data is large, the learning algorithm behaves better:Using a larger set of training (which means that it
Source: From Machine learningThis paper first introduces the trend of Internet community and machine learning Daniel, and the application of machine learning, then introduces the machine learn
Machine learning Algorithm and Python Practice (c) Advanced support vector Machine (SVM)Machine learning Algorithm and Python Practice (c) Advanced support vector Machine (SVM)[Email protected]Http://blog.csdn.net/zouxy09Machine
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