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him run faster, just rewrite the performance bottleneck and embed it in C + +.
Links:
SciPy Stack (http://www.scipy.org/getting-started.html)-General tasks
Spark (http://spark.apache.org/docs/latest/index.html)-Going large
TensorFlow (http://www.tensorflow.org/)-going deep github-jupyter/docker-stacks:opinionated stacks of Ready-to-run Ju Pyter applications in Docker.
Python's fast iterative capabilities allow it to receive favor.
1 crawl
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In 2013, Nal Kalchbrenner and Phil Blunsom presented a new end-to-end encoder-decoder architecture for machine translation. In 2014, Sutskever developed a method called sequence-to-sequence (seq2seq) learning, and Google used this model to give a concrete implementation method in the tutorial of its deep learning framework
major (he transferred from computer science to mathematics major).Machine learning has many directions.Machine learning inside, especially in industry. Machine learning is dismembered into many directions, for example, some people do data processing, some people specialize
* *.Second, installation Scikit-learnExecute command:Conda Install Scikit-learnSecond, installation KrasExecute command:Conda Install KerasThe required tensorflow is automatically installation during installation of the Keras process.At this point, deep learning, machine learning development environment has been insta
more to it than that: all learning is constrained by the collection of parallel text blocks. The deepest neural network is still learning in the parallel text. If you do not provide resources to the neural network, it will not be able to learn. And humans can expand their vocabulary by reading books and articles, even if they don't translate them into their native language.If humans can do that, neural net
Dialogue machine learning Great God Yoshua Bengio (Next)Professor Yoshua Bengio (Personal homepage) is one of the great Gods of machine learning, especially in the field of deep learning. Together with Geoff Hinton and Professor Yann LeCun (Yan), he created the deep
say we have some data points, and now we use a straight line to fit these points, so that this line represents the distribution of data points as much as possible, and this fitting process is called regression.In machine learning tasks, the training of classifiers is the process of finding the best fit curve, so the optimization algorithm will be used next. Before implementing the algorithm, summarize some
problem solution.Or simply, it can be understood that finding a reasonable hyper-plane in a high-dimensional space separates the data points, which involves the mapping of non-linear data to high-dimensional to achieve the purpose of linear divisible data. The above sample map is a special two-dimensional situation, of course, the real situation may be many dimensions. Start with a simple understanding of what a support vector is at a low latitude. Can see 3 lines, the middle of the red li
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 loo
This article is a computer Quality Pre-sale recommendation >>>>"machine learning Practice test-driven development method"A reliable and stable machine learning algorithm is developed using test-driven method.Editor's recommendationThis book describes how to use a test-driven
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 this is the worst case (log2100log2100) can be guessed six or seven times. This is a binary search, which may be used in life, and ofte
, powerful and concise. For information on getting started, you can take a look at CSDN's article on Light ink. The address of his bloghttp/Blog.csdn.net/poem_qianmo?viewmode=contents , this is my OEPNCV entry information, a total of 18 lessons, down with sit down, should be able to get started, if not enough, can buy his book, and I Love a book"In-depth understanding of OpenCV"。2. About
To learn about machine learning, you must master a few mathematical knowledge. Otherwise, you will be confused (Allah was in this state before ). Among them, data distribution, maximum likelihood (and several methods for extreme values), deviation and variance trade-offs, as well as feature selection, model selection, and hybrid model are all particularly important. Here I will take you to review the releva
subject of which is a computer scientist. Now "machine learning researchers" may have very few people who read the 1983 Learning:an Artificial Intelligence approach book. The publication of this book marks the beginning of machine learn
unknown, even if you understand the operating principles of algorithms, you cannot write your own code independently. It can only be written based on the code in the book. I want to know how to turn this knowledge into the ability to write your own code. I want to work on machine learning or data mining in the future. Reply content: first, practice Python. After
learning more effective, able to build a more intelligent system. We all agree that intelligence is an inevitable trend in the development of computer science, making our computers more and more intelligent. In this process, we must have a very powerful means. So far, in other fields of artificial intelligence, we find that the most powerful means may be based on data. Machine
Microsoft internal windows,bing and Azure are currently in the preview phase, with the latest version 0.2. The learning models currently supported by the framework include
K-means Clustering
Logistic regression
Support Vector Machine
Naive Bayesian
Random Forest
Tree enhancement
Other technologies, such as recommendation engines and anomaly detection, are being developed o
Fortunately with the last two months of spare time to "statistical machine learning" a book a rough study, while combining the "pattern recognition", "Data mining concepts and technology" knowledge point, the machine learning of some knowledge structure to comb and summarize
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