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In order for you to better learn PHP, I mainly want to talk about the PHP Learning line this time so that you can better learn PHP. this time I mainly want to talk about the PHP Learning line, sort out the technical requirements of PHP programmers at each stage, and set the learning and growth goals based on your own situation.

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 of business intelligence. Microsoft's goal is

I hear that Hulu machine learning is better than a winter weekend.You can click "Machine Learning" in the menu bar to review all the previous installments of this series and comment on your thoughts and comments.At the same time, in order to make everyone better understand Hulu, the menu "about Hulu" also made the corresponding adjustment, curious babies, brand turn up bar!Today's content is"Unsupervised

A blink of an eye, from the beginning of contact with machine learning, to now be trivial about, have to put down Hangyuan Li Teacher's "statistical learning method", nearly five months. For five months, the first one months were the happiest of my time, and it was wonderful to enjoy all the thinking that the statistical learning method brings. By the end of the

Machine learning system Design (Building machines learning Systems with Python)-Willi Richert Luis Pedro Coelho General statementThe book is 2014, after reading only found that there is a second version of the update, 2016. Recommended to read the latest version, the ability to read English version of the proposal, Chinese translation in some places more awkward (but the English version of the book is indee

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 performance. Each space corresponds to a distance metri

information table, X indicates that the dimensions of the high dimensional input matrix are the high dimension D times the number of samples N, C=xxt, Z represents the dimension reduction output matrix size low dimension d times N, E=zzt, the linear mapping is Z=WTX, the distance matrix between 22 in the high-dimensional space is a, and the SW,SB is LDA respectively. In-class divergence matrices and inter-class divergence matrices, K indicates that a point in manifold

The previous article mentioned the importance of error analysis and setting error metrics. That is to set a real number to evaluate the learning algorithm and measure its performance. With the evaluation and error metrics of the algorithm, one important thing to note is that using an appropriate error metric can sometimes have a very subtle effect on the learning algorithm. This kind of problem is the probl

understand the task, so "save the Earth" to understand "kill all human beings." This is like a typical predictive algorithm that literally understands the task and ignores the other possibilities or the practical significance of the task.So, in January 2016, Harvard Business School professor Michael Luca, professor of economics Sendhil Mullainathan, and Cornell University professor Jon Kleinberg, published an article titled "Algorithm and Butler" in the Harvard Commercial Review. Call upon the

Reproduced http://blog.csdn.net/zhoutongchi/article/details/8191991 Learning ing functions and literature applied in behavior recognition/image classification (models and non-models are associated with each other, and algorithms are mutually adopted. There is no clear distinction between them, including the bionic literature) %The research focuses on ICA model and deep learning with sparse encoding. 1) spar

Unity learning Summary 4. unity learning Summary Silence for a long time, busy for a long time, starting from the beginning is still trying to find excuses for failing to make a good summary and thinking. To sum up, time can still be drawn out. Recently, the accumulation of pitfalls seems to be almost enough. This is also equivalent to leaving more attention to solving problems in the future. I hope you can