ibm machine learning api

An exploration of AWS Machine Learning (1): comprehend-natural language processing service 1. Comprehend Service Introduction 1.1 features The Amazon comprehend service uses natural language processing (NLP) to analyze text. Its use is very simple. Input: text in any UTF-8 format Output: Comprehend outputs a set of entities (entity), a number of keywords (key phrase), which language (Language), w

Sichuan University may have a mainframe, so we will first take us to the machine room to see the true face of the mainframe, and then start to explain the history of the mainframe with that excitement and freshness, and finally we will go into the specific content, I really didn't expect it to be such a teacher and so boring to explain what it has to do with PC. According to the instructor's explanation, I did not know much about the system as I did

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

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

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

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：

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

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