watson machine learning api

Selected top 32 machine learning open source project, organized from Mybridge AI: 1. Fasttext: Quick text representation and text classification library (11786 stars on GitHub, contributor Facebook) SOURCE Link: Https://github.com/facebookresearch/MUSE 2. Deep-photo-styletransfer: "Deep photo Style Transfer" The source and data of this paper. (GitHub 9747 stars, papers from Cornell University's Fujun Luan)

)Three use Jconsole monitor the running state1. We plan to set the total of the heap too small to 20M, the new generation and the old age of 10M. Because Survivor and Eden have a space ratio of 1:8 by default, Eden is about 8m,2 survivor each 1M. The following verifies that Jconsole's monitoring is consistent with our plans.(1) Total size of the heap:(2) Old age Size:(3) Eden Size:(4) Survivor size:Result: The expected plan was met.2. Other information3. Cons: Jconsole will have an impact on the

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

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

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

Recently TalkingData Open source The main role of Fregata,fregata is to speed up the computing speed of machine learning based on spark, it is said that 1 billion * 1 billion level of data if cached in memory, the 1s clock can be completed, if not cached, 10 seconds to fix, If this is the case, it is a fortress, and the following are only translations, if there are incorrect welcome corrections Brief introd

There is no perfect program in the world, but we are not frustrated because writing a program is a process that is constantly striving for perfection.Advantages of Java:(1) write in sequence, run in multiple places(2) provides a relatively secure memory management and access mechanism to avoid the vast majority of memory leaks and pointer cross-border issues(3) Hot spot Code detection and runtime compilation and optimization, which allows the Java application to achieve higher performance with t

Using Python3 to learn the API of linear regressionPrediction of benign and malignant tumors using logistic regression and stochastic parameter estimation regression respectivelyI downloaded the dataset locally and can come to my git to download the source code and dataset:Https://github.com/linyi0604/kaggle1 ImportNumPy as NP2 ImportPandas as PD3 fromSklearn.cross_validationImportTrain_test_split4 fromSklearn.preprocessingImportStandardscaler5 fro

In the process of learning machine learning algorithms, we often need data to validate algorithms and debug parameters. But it's not that easy to find a set of data samples that are perfectly suited to a particular type of algorithm. Fortunately NumPy, Scikit-learn all provide the function of random data generation, we can generate data for a certain model oursel

minimum value (that is, the best fit to the data) fp1, residuals, rank, sv, rcond = sp.polyfit(x,y,1,full =True) print fp1 FP1 is a two-dimensional array with values of A and B. The printed value is [2.59619213, 989.02487106]. We obtain the linear function f (x) = 2.59619213x + 989.02487106. What is its error? Do you still remember the error function? We construct a function using the following code: f1 = sp.poly1d(fp1)print (error(f1,x,y)) We get a result: 317389767.34 is the result? Not

Misc.forsale 0.91 0.70 0.79 257 the Rec.autos 0.89 0.89 0.89 238 - Rec.motorcycles 0.98 0.92 0.95 276 - Rec.sport.baseball 0.98 0.91 0.95 251 the Rec.sport.hockey 0.93 0.99 0.96 233 the Sci.crypt 0.86 0.98 0.91 238 the sci.electronics 0.85 0.88 0.86 249 the sci.med 0.92 0.94 0.93 245 - sci.space 0.89 0.96 0.92 221 the Soc.religion.christian 0.78 0.96 0.86 232 the talk.politics.guns 0.88 0.96 0.92 251 the talk.politics.mideast 0.90 0.98 0.94 23194 Talk.politics.misc 0.79 0.89 0.84 188 the Talk.r

AboutDTC =Decisiontreeclassifier () $ #Training - Dtc.fit (X_train, Y_train) - #Predicting saved results -Y_predict =dtc.predict (x_test) A + " " the 4 Model Evaluation - " " $ Print("accuracy:", Dtc.score (X_test, y_test)) the Print("Other indicators: \ n", Classification_report (Y_predict, Y_test, target_names=['died','survived'])) the " " the accuracy: 0.7811550151975684 the Other indicators: - Precision recall F1-score support in the died 0.91 0.78 0.84 236 the survived 0.58 0.80 0.67 Abo

", Classification_report (Gbc_y_predict, Y_test, target_names=['died','survived']))103 104 " " the Single decision tree accuracy: 0.7811550151975684106 Other indicators:107 Precision recall F1-score support108 109 died 0.91 0.78 0.84 236 the survived 0.58 0.80 0.67111 the avg/total 0.81 0.78 0.79 329113 the Random forest accuracy: 0.78419452887538 the Other indicators: the Precision recall F1-score support117 118 died 0.91 0.78 0.84 237119 survived 0.58 0.80 0.68 - 121 avg/total 0.82 0.78 0.79

and write access to the variable requires ignoring the local cache and manipulating the memory directly. However, each change access is made to cross the memory fence and eventually degrade the program performance. Also, in scenarios where multiple fields are accessed concurrently by multiple threads, the volatile keyword cannot guarantee the atomicity of the overall operation because the access to each volidate field is handled independently and cannot be uniformly coordinated into one access.

, first of all to register the NVIDIA Development Account, then can download CUDNN.To put it simply, a few files are copied: library files and header files. Copy the CUDNN header file to/usr/local/cuda/lib64 and copy the CUDNN library file to/usr/local/cuda/include.After downloading the CD into the file package directory, unzip the file:TAR-ZXF cudnn-7.0-linux-x64-v4. 0-prod.tgzcd cuda#链接到cuda的库里sudo cp lib64/* /usr/local/cuda/lib64/sudo CP include/cudnn.h/usr/local/cuda/include/要不要链接cuDNN的库文件：