0.5% of the patients in our screening program are suffering from cancer. In this case, the error rate of 1% is no longer as good.For example, here is a line of code that ignores the input value x, so that y is always equal to 0, so it always predicts that no one has cancer. Then this algorithm actually has only 0.5% error rate. So this is even better than the 1% error rate we got before, which is a non-machine le
Original writing. For reprint, please indicate that this article is from:Http://blog.csdn.net/xbinworld, Bin Column
Pattern Recognition and machine learning (PRML), Chapter 1.2, probability theory (I)
This section describes the essence of probability theory in the entire book, highlighting an uncertainty understanding. I think it is slow. I want to take a look at it and write the blog code, but I want t
Learning notes of machine learning practice: Classification Method Based on Naive Bayes,
Probability is the basis of many machine learning algorithms. A small part of probability knowledge is used in the decision tree generation process, that is, to count the number of time
mathematical expression was unfolded using Taylor's formula, and looked a bit ugly, so we compared the Taylor expansion in the case of a one-dimensional argument.You know what's going on with the Taylor expansion in multidimensional situations.in the [1] type, the higher order infinitesimal can be ignored, so the [1] type is taken to the minimum value,should maketake the minimum-this is the dot product (quantity product) of two vectors, and in what case is the value minimal? look at the two vec
Tags: deviation chinese data cts You multitasking performance GPO ESCLearning Goals
Understand what multi-task learning and transfer learning is
Recognize bias, variance and data-mismatch by looking in the performances of your algorithm on train/dev/test sets
"Chinese Translation"Learning GoalsLearn what multi-tasking
Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-Job three q6-10 C + + implementation. Although there are many great gods in many blogs have given the implementation of Phython, but given the C + + implementation of the article is significantly less, here for everyone to prov
Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-Job three q18-20 C + + implementation. Although there are many great gods in many blogs have given the implementation of Phython, but given the C + + implementation of the article is significantly less, here for everyone to pro
After tossing the crawler and some interesting content, I recently in the R language for simple machine learning knowledge, the main reference is "machine learning-Practical Case Analysis" this book.This book is a rare, purely r language-based machine
Machine learning Algorithms Study NotesGochesong@ Cedar CedroMicrosoft MVPThis series is the learning note for Andrew Ng at Stanford's machine learning course CS 229.Machine learning Al
computer, and each instruction represents one or more operations.Give a simple example, and you can use it in your life. Now make a small game, a on the paper randomly wrote a 1 to 100 integer, b to guess, guess the game is over, guess the wrong word a will tell B guess small or big. So what will b do, the first time you must guess 50, guess the middle number. Why is it? Because of this worst case scenario (log2100">Log2log2100) Six or seven times can be guessed.This is a binary search, which m
Python Machine Learning Theory and Practice (4) Logistic regression and python Learning Theory
From this section, I started to go to "regular" machine learning. The reason is "regular" because it starts to establish a value function (cost function) and then optimizes the val
converge or even diverge. .One thing worth noting:As we approach the local minimum, the guide values will automatically become smaller, so the gradient drop will automatically take a smaller amplitude, which is the practice of gradient descent. So there's actually no need to reduce the alpha in addition, we need a fixed (constant) learning rate α. 4. Gradient Descent linear regression (Gradient descent for Linear Regression) This is the method of us
:
Random initialization
Loop until convergence {
Each State transfer count in the sample is used to update and R
Use the estimated parameters to update V (using the value iteration method of the previous section)
According to the updated V to re-draw
}
In step (b) We are going to do a value update, which is also a loop iteration, in the previous section we solved v by initializing v t
The fate of life, strange and difficult to test.I thought the time was devoted to Java, but did not want to break into the hall of machine learning. That summer, the scorching sun, across 1000 kilometers to the strange city of wandering, I hope all this is worthwhile.I Java origin, slightly understand c,linux, database, technology slag slag.Hope every step of life is a new starting point, each step has a ne
meaning of these methods, see machine learning textbook. One more useful function is train_test_split.function: Train data and test data are randomly selected from the sample. The invocation form is:X_train, X_test, y_train, y_test = Cross_validation.train_test_split (Train_data, Train_target, test_size=0.4, random_state=0)Test_size is a sample-to-account ratio. If it is an integer, it is the number of sam
Hello everyone, I am mac Jiang, first of all, congratulations to everyone Happy Ching Ming Festival! As a bitter programmer, Bo Master can only nest in the laboratory to play games, by the way in the early morning no one sent a microblog. But I still wish you all the brothers to play happy! Today we share the coursera-ntu-machine learning Cornerstone (Machines learning
1. Nearest Neighbor Component analysis (NCA) algorithmAbove content reproduced from: http://blog.csdn.net/chlele0105/article/details/130064432. Metric LearningIn machine learning, the main purpose of dimensionality reduction of high dimensional data is to find a suitable low-dimensional space, in which the learning can be better than the original space performanc
Reprinted from: Http://www.cnblogs.com/shishanyuan/p/4747761.html?utm_source=tuicool1. Machine Learning Concept1.1 Definition of machine learningHere are some definitions of machine learning on Wikipedia:L "Machine
two classification problem, so the model is modeled as Bernoulli distributionIn the case of a given Y, naive Bayes assumes that each word appears to be independent of each other, and that each word appears to be a two classification problem, that is, it is also modeled as a Bernoulli distribution.In the GDA model, it is assumed that we are still dealing with a two classification problem, and that the models are still modeled as Bernoulli distributions.In the case of a given y, the value of x is
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
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