information gain
Building a decision Tree
Random Forest
K Nearest neighbor--an algorithm of lazy learning
Summarize
The fourth chapter constructs a good training set---data preprocessing
Handling Missing values
Eliminate features or samples with missing values
Overwrite missing values
Understanding the Estimator API in Sklearn
Working with
["Predictions"] = []# Loop over the results and add them to the list of# returned predictions for(Imagenetid, label, prob)inchresults[0]: R = {"Label": Label,"Probability":float(Prob)} data["Predictions"].append (R)# indicate that's the request was a successdata["Success"] =True# Return the data dictionary as a JSON responsereturnFlask.jsonify (data)Although it is a core part, it is very easy to be reused. is the process of reading the data and then processing it. # If This is the main thread o
In-depth spark machine learning combat (user behavior analysis)Course View Address: http://www.xuetuwuyou.com/course/144The course out of self-study, worry-free network: http://www.xuetuwuyou.comI. Objectives of the courseMaster the various operations of sparksql in-depth understanding of spark's internal implementation principlesLearn more about the construction and operation of various algorithmic models
sophisticated machine learning library, widely used in industry and academia. One thing about Scikit-learn very impressive is that it maintains a very consistent "fit", "predictive" and "test" APIs in many numerical techniques and algorithms, making it very easy to use. In addition to this consistent API design, Scikit-learn also provides some useful tools for d
linear algebra and similar to numpy arrays.DecafDecaf is a recent deep learning library published by UC Berkeley, tested in the Imagenet Classification challenge, and its neural network implementation is very advanced (state of art).NolearnIf you want to use the excellent Scikit-learn Library API in deep learning, encapsulating the decaf Nolearn will make it eas
neural networks through different configuration files. IX, Hebel hebel is a neural network library with GPU support that can determine the properties of a neural network through YAML files. Provides a way to separate the Divine Network and code-friendly, and run the model quickly, it is written in pure Python, is a very friendly library, but because of the development soon, on the depth and the vast, there is some lack! ten, Neurolab neurolab is an API
Ten classic algorithms in machine learning and Data Mining
Background:
In the early stage of the top 10 algorithm, Professor Wu made a report on the top 10 challenges of Data Mining in Hong Kong. After the meeting, a mainland professor put forward a similar idea. Professor Wu felt very good and began to solve the problem. I found a series of big cows (both big cows for data mining) and thought they were doi
recursive neural network-based text notation word2vec. v. Orange VI, PyMVPA Vii. Theano Viii. PyLearn IX, Hebel ten, Neurolab neurolab is an API-friendly neural network library that contains different variants of the recursive neural network implementation, If you use RNN, this library is one of the best choices in a homogeneous API. python Development Engineer must know ten
want to go down the hill, how can you go down the hill as soon as possible (by default, the speed is constant and you will not die )?
You should look around and find the steep current direction to go down the hill? In this direction, the gradient can be used for calculation, which is the source of the gradient descent method. Do you think it is very simple, think you have mastered it? Haha, it's still too young.
I will not go into details about this part. I will provide two materials for my stu
Recently, the Facebook Artificial Intelligence Research Institute (FAIR) has open-source a series of software libraries to help developers build larger, faster, Gigaom reports.
Model of depth learning. The Open Software Library is called a module on Facebook. Using them to replace the torch in the common development environment in the field of machine learning
Writing programming and writing machine learning modelsBased on the different machine learning models, a large number of characteristic variables are used to predict the fluctuation of the underlying asset price, and the prediction results are evaluated. Machine
Analysis of malware through machine learning: Basic Principles of clustering algorithms in Deepviz
Since last year, we have discovered that many audiovisual companies have begun to engage in machine learning and artificial intelligence, hoping to find a fast and effective way to analyze and isolate new types of malware
Python3 Learning API UsagePrincipal component analysis method for reducing dimensionUsing the data set on the network, I have downloaded to the local, can go to my git referenceGit:https://github.com/linyi0604/machinelearningCode:1 fromSklearn.svmImportlinearsvc2 fromSklearn.metricsImportClassification_report3 fromSklearn.decompositionImportPCA4 ImportPandas as PD5 ImportNumPy as NP6 " "7 principal compo
from:http://www.erogol.com/broad-view-machine-learning-libraries/Http://www.slideshare.net/VincenzoLomonaco/deep-learning-libraries-and-rst-experiments-with-theanoFebruary 6, EREN 1 COMMENT Especially, with the advent of many different and intricate machine learning algorit
The basis of text sentiment analysis is natural language processing, affective dictionary, machine learning method and so on. Here are some of the resources I've summed up.Dictionary resources:Sentiwordnet"Knowledge Network" Chinese versionChinese Affective polarity dictionary NTUSDEmotion Vocabulary Ontology DownloadNatural language processing tools and platforms:Institute of Social Computing and Informati
As an open-source cluster computing environment, Spark has a distributed, fast data processing capability. The mllib in spark defines a variety of data structures and algorithms for machine learning. Python has the Spark API. It is important to note that in spark, all data is handled based on the RDD.Let's start with a detailed application example of clustering K
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B. Complete guide to deep learning in Python TensorFlow (recommended 17,834, 4.6/5 stars)
Links: Http://bit.ly/2EatVy7
Next is a selection of top 30 items from the Mybridge:
1.FastText: Fast text representation and text classification library (11786 stars on GitHub, contributors Facebook
SOURCE Link: Https://github.com/facebookresearch/MUSE
2.deep-photo-styletransfer: "Deep photo Style Transfer"The source code and data of this paper. (GitHub
Boring, adapt to the trend, learn the Python machine learning it.Buy a book, first analyze the catalogue it.1. The first chapter is the Python machine learning ecosystem.1.1. Data science or machine learning workflow.It is then di
Brief introduction
In recent years, because of the cloud platform, large data, high-performance computing, machine learning and other areas of progress, artificial intelligence also fire up. Face recognition, speech recognition and other related functions have been proposed, but can form products and large-scale use of small. Because it is difficult for non-professional professionals to achieve a complete s
universities and training and learning related professional teachers and students teaching reference.Directory1th Chapter Spark1.1 Big Data Age1.2 Big Data Analysis era1.3 Simple, elegant, effective-this is spark1.4 Core--mllib1.5 A single spark can be a prairie fire1.6 Summary2nd. Spark installation and development environment configuration2.1 Windows Standalone mode spark installation and configuration2.1.1 Windows 7 Installation Java2.1.2 Windows
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