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is mainly to learn the probability distributions of words, phrases and sentence sequences. You can take a look at Richard Socher's work, which is very deep. can also look at the work of Tomas Mikolov, he defeated the world record of language model with the recursive neural network, he studied the distribution, to some extent, revealed some nonlinear relationship between words. For example, if you subtract the attribute vector of "Roman" with the attr
ContentA simple overview of machine learningThe main task of machine learningLearning the causes of machine learningThe advantages of the Python languagemachine learning allows us to inspired by the data set, in other words, we use computers to demonstrate the true meaning behind the data , which is
Octave Machine Learning Common commands A, Basic operations and moving data around 1. Attach the next line of output with SHIFT + RETURN in command line mode 2. The length command returns a higher one-dimensional dimension when apply to the matrix 3. Help + command is a brief aid for displaying commands 4. doc + command is a detailed help document for displaying commands 5. Who command displays all current
This paper is organized from the "machine learning combat" and Http://write.blog.csdn.net/posteditBasic Principles of Mathematics: Very simply, the Bayes formula: Base of thought: For an object to be sorted x, the probability that the thing belongs to each category Y1,y2, which is the most probability, think that the thing belongs to which category.Algorithm process: 1. Suppose something to be sorted x, it
says that the machine learning system is very complex to build, if inexperienced, or not cautious, in many parts of the easy "debt", these debts were not affected at the time, but because the "interest" is very high, it will make you more miserable later.I read the article according to the article self-organized, technical debt of several specific dimensions. These dimensions and our own practice is also h
is that only the input paradigm is provided for this network, and it automatically identifies its potential class rules from those examples. When the study is complete and tested, it can also be applied to new cases. A typical example of unsupervised learning is clustering. The purpose of clustering is to bring together things that are similar, and we do not care what this class is. Therefore, a cluste
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
0. Training Data set: Iris DataSet (Iris DataSet), get URL Https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.dataAs shown, the first four columns of each row of data in the IRIS data set are the petal length/width, the calyx length/width, and the iris in three categories: Setosa,versicolor,virginicaYou can save the dataset with the following example
imagenet by deep learning, and the deep learning model, represented by CNN, is now a bit exaggerated, borrowed from the Chinese University of Hong Kong Prof. Xiaogang Wang Teacher's summary article, Deep learning is nothing more than the traditional machine feature learning
a summary of KNN algorithm KNN classification algorithm is simple and effective, can be classified and return.Core principle: The characteristics and classification of each data of a given sample dataset, the characteristics of the new data and the sample data are compared to find the most similar (nearest neighbor) K (k in short: Birds of a feather flock together second, for example: As shown in the following illustration: Blue squares and red trian
new feature $f$ given the $x$ of a data point. When $\THETA^TF \geq 0$, predict $y=1$, and conversely, predict $y=0$.Training (Training): $$\min\limits_\theta c\left[\sum\limits_{i=1}^{m}y^{(i)}cost_1 (\theta^tf^{(i)}) + (1-y^{(i)}) Cost_0 ( \theta^tf^{(i)}) \right] + \frac{1}{2}\sum\limits_{j=1}^{n}\theta_{j}^2$$Effect of parameter C ($\approx\frac{1}{\lambda}$): Large c:low bias, high variance Small c:high bias, low variance Effect of parameter $\sigma^2$: Large $\s
SVM is mainly due to the introduction of nonlinear kernel functions. But new problems continue to arise, and these problems involve different areas of knowledge and business scenarios, often relying on only a few common kernel function does not solve the problem. However, SVM relies too much on kernel functions, and there are many limitations of kernel functions, and its flexibility is certainly inferior to that of artificial feature construction methods. On the other hand, with the increasing
, the whole story line is chaotic, not clear, than I have seen before the film is far away, character's character has not shown, the key is the film theme is also biased; The film is really good, the plot and character are very vivid, and the scene is very lifelike, the protagonist's strength performance coupled with his innate melancholy look at the characters to live.Give us an example of unsupervised learning
There is a period of time does not dry goods, home are to be the weekly lyrics occupied, do not write anything to become salted fish. Get to the point. The goal of this tutorial is obvious: practice. Further, when you learn some knowledge about machine learning, how to deepen the understanding of the content through practice. Here, we make an example from the 2nd
in formula 4: (Formula 4) Obtain the formula shown in (formula 5) by using the formula of the Laplace multiplier: (Formula 5) In formula 5, we use the Laplace multiplier function to evaluate the derivation of W and B, respectively. To obtain the extreme point, let the derivative be 0 and get And then place them in the formula (formula 6) of the Laplace multiplier: (Formula 6) (Formula 6) the last two rows are the optimization functions to be solved. Now we only need to make a secondary pla
) Music tag script under music Tagging-torch7 Torch-datasets reads scripts for several popular datasets, including: Bsr500 CIFAR-10 Coil Street View House Numbers Mnist Norb Atari2600-generate a dataset script using static frames in the arcade learning environment simulator. MATLAB Computer Vision Contourlets-Matlab source code for implementing contour Wave Transformation and us
data and play games purposefully. A famous game is called esp. Two gamers will show them a picture at the same time, ask them to mark the images at the same time. If they are marked with the same key word or mark, they both score. If they are not consistent, they do not score. In this way, both players want to mark the image accurately so that they can score. According to our general knowledge, two people will try to find a proper common sense tag to mark the tag on the image. The markup on ima
Brief introductionBefore I introduce machine learning, I would like to start by listing some examples of machine learning: junk e-mail detection: Identifies what is spam and what is not, based on the messages in the mailbox. Such a model can help categorize spam and non-spam messages by programs. This
standard algorithm engineer should have the ability, of course, I here is the search algorithm for example, the other algorithm engineers are not too much, always run but the above several processes, of course, if you are a cow, can modify the machine according to the scene model of learning, and even can think of a model, it is more powerful. OK, let's take a l
Machine learning Types Machine Learning Model Evaluation steps Deep Learning data Preparation Feature Engineering Over fitting General process for solving machine learning
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