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
There are two definitions related to machine learning:1) give the computer the research field of learning ability without fixed programming.2) A computer program that can learn from a number of tasks (T) and performance metrics (P), Experience (E). In learning, the performance p of task t can improve experience E with
This topic (Machine Learning) including Single-parameter linear regression, multi-parameter linear regression, Octave tutorial, logistic regression, regularization, neural network, machine learning system design, SVM (Support Vector Machines support vector
11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data
11.1 what to do firstIn the next video, I'll talk about the design of the machine learning system. These videos will talk about the major problems you will encounte
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
Azure Machine Learning ("AML") is a Web-based computer learning service that Microsoft has launched on its public cloud azure, a branch of AI that uses algorithms to make computers recognize a large number of mobile datasets. This approach is able to predict future events and behaviors through historical data, which is significantly better than traditional forms
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
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
First, the visualization method
Bar chart
Pie chart
Box-line Diagram (box chart)
Bubble chart
Histogram
Kernel density estimation (KDE) diagram
Line Surface Chart
Network Diagram
Scatter chart
Tree Chart
Violin chart
Square Chart
Three-dimensional diagram
Second, interactive tools
Ipython, Ipython Notebook
plotly
Iii. Python IDE Type
Pycharm, specifying a Java swing-based user interface
PyDev, SWT-based
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
Learning notes of machine learning practice: Implementation of decision trees,
Decision tree is an extremely easy-to-understand algorithm and the most commonly used data mining algorithm. It allows machines to create rules based on datasets. This is actually the process of machine
Brief introductionMachine learning algorithms are algorithms that can be learned from data and improved from experience without the need for human intervention. Learning tasks include learning about functions that map input to output, learning about hidden structures in unlabeled data, or "instance-based
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
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
meaningless.Thus, further, the following derivation is made:As for why we use the 2 norm here, I understand mainly for the sake of presentation convenience.The meaning of such a big paragraph after each round of algorithm strategy iteration, we require the length of the W to increase the growth rate is capped. (Of course, it is not necessarily the growth of each round, if the middle of the expansion of the equation is relatively large negative, it may also decrease)The above two ppt together to
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