项目作者: ssp4all

项目描述 :
Data wrangling, Apache Spark for recommendation systems, Bitcoin prediction using regression, NLP for sentimental analysis and Deep Learning for defect detection from textured surfaces.
高级语言: HTML
项目地址: git://github.com/ssp4all/machine-learning-for-business-applications.git


The Ring_1647968706254.pdf
UX paper presentation_1647968706389.pdf
00.Optimization.Primer_1647968642660.pdf
01.Optimization.SGD_1647968642717.pdf
02.SGD-tricks-Microsoft-2012_1647968642763.pdf
04.DeepLearning.NN_1647968642829.pdf
05.DeepLearning.Backpropagation_1647968643067.pdf
06.DeepLearning.Core.DBNN.Pruning_1647968643294.pdf
07.DeepLearning.Regularization.CNN.RNN_1647968643528.pdf
08.DeepLearning.ParameterOptimization_1647968643638.pdf
01.ProjectDescription-GraphEmbedding_1647968645618.pdf
Project DescriptionNetworkPropertiesSpark_1647968646172.pdf
rshah27_BayesianParameterEstimation_1647968646587.pdf
spawar2pdf_1647968646731.pdf
Project-AdWords.BipartiteGraphMatching_1647968646806.pdf
01.GeneralizedLinearModel_1647968647052.pdf
02.BayesianInference_1647968647179.pdf
03.ParameterEstimation.MLE.Bayesian.Part-1_1647968647668.pdf
04.ParameterEstimation.MLE.Bayesian.Part-2_1647968647754.pdf
01.SparkStreaming_ProjectDescription_1647968648398.pdf
Intro2StatLearning_1647968648890.pdf
01.ProjectDescription-SentimentAnalysis_Word2Vec_Doc2Vec_1647968649551.pdf
results_1647968656806.pdf
(DUE_ 01_23_2020) SUBMIT_ QUIZ_ Alternating Least Squares (ALS)_ Attempt review_1647968656986.pdf
(DUE_ 01_23_2020)_ SUBMIT_ QUIZ_ Data Streaming Principles_ Attempt review_1647968657027.pdf
(DUE_ 01_23_2020)_ SUBMIT_ QUIZ_ Generalized Linear Models_ Basics_ Attempt review_1647968657060.pdf
(DUE_ 01_30_2020)_ SUBMIT_ QUIZ_ Bayesian Inference_ Attempt review_1647968657081.pdf
00.Tutorial-Apache_Spark_1647968657131.pdf
01.ApacheSparkCore_1647968657176.pdf
01.DataStreaming.Principles_1647968657364.pdf
02.SparkSQL.DataFrames_1647968657402.pdf
02.SparkStreaming_1647968657504.pdf
03.KeyValue.PairedRDDs_1647968657532.pdf
03.Spark.Load.Save.Data_1647968657593.pdf
04.Kafka-Spark-MongoDB-distribution_1647968657713.pdf
0.Python-NumPy_1647968657773.pdf
1.Importing_Data_Python_Cheat_Sheet_1647968657820.pdf
10.PythonScikit-Learn-ML_1647968657908.pdf
11.Python-SciPy-LinearAlgebra_1647968658610.pdf
12.Python-Keras-NeuralNetworks_1647968658695.pdf
2.PandasBasics_4_DataScience_1647968658825.pdf
3.PythonPandas-4-DataScience_1647968658891.pdf
4.Python-Basics_1647968658978.pdf
5.Python-JupyterNotebook_1647968659045.pdf
6.python-regular-expressions-cheat-sheet_1647968659105.pdf
7.PythonVisualization_Matplotplotlib_1647968659146.pdf
8.PythonVisualization_Seaborn_1647968659258.pdf
9.PythonVisualization_Bokeh_1647968659417.pdf
Autoencoders_1647968659485.pdf
CNN_1647968659547.pdf
Recurrent Neural Networks_1647968659753.pdf
Recurrent_Neural_Network_1647968659839.pdf
Word2Vec Presentation.pptx_1647968659961.pdf
word2vecParameterLearningExplained_1647968660226.pdf
Autoencoders_1647968660281.pdf
CNN_1647968660367.pdf
Recurrent Neural Networks_1647968660704.pdf
Recurrent_Neural_Network_1647968660749.pdf
Word2Vec Presentation.pptx_1647968660878.pdf
word2vecParameterLearningExplained_1647968660902.pdf
00.Optimization.Primer_1647968661075.pdf
01.Optimization.SGD_1647968661111.pdf
02.SGD-tricks-Microsoft-2012_1647968661203.pdf
04.DeepLearning.NN_1647968661296.pdf
05.DeepLearning.Backpropagation_1647968661439.pdf
06.DeepLearning.Core.DBNN.Pruning_1647968661522.pdf
07.DeepLearning.Regularization.CNN.RNN_1647968661656.pdf
08.DeepLearning.ParameterOptimization_1647968661694.pdf
00.Statistics.Glossary.Samatova_1647968663673.pdf
01.A-B_Testing.Samatova_1647968663707.pdf
02.Sampling.Samatova_1647968663832.pdf
03.Power_SampleSize_Type_I_II_Errors.Samatova_1647968663874.pdf
04.MultipleTesting.Samatova_1647968663907.pdf
05.HypothesisTesting.Samatova_1647968663968.pdf
06.Distributions.ConfidenceIntervals.Samatova_1647968664048.pdf
07.HypothesisDesign.DesignExperiments.Samatova_1647968664126.pdf
08.ModelIntercomparison.Samatova_1647968664276.pdf
ch19.DesignAnalysis.ML.Experiments_1647968664749.pdf
01.GeneralizedLinearModel_1647968665621.pdf
02.BayesianInference_1647968665723.pdf
03.ParameterEstimation.MLE.Bayesian.Part-1_1647968665899.pdf
04.ParameterEstimation.MLE.Bayesian.Part-2_1647968665950.pdf
01.AdWords.BipartiteGraphMatching_1647968666067.pdf
02.CommunityDetection.AttributedGraphs_1647968666093.pdf
03.Word2Vec.Doc2Vec_1647968666285.pdf
04.DeepWalk.HeterogeneousDataFusion_1647968666478.pdf
02-03.Topic-I.AlternateLeastSquares.RecommendationSystems_1647968666650.pdf
02.Topic-I.AlternateLeastSquares.RecommendationSystems.Part-1_1647968666747.pdf
01.CausalInference_1647968666928.pdf
01.TS-Forecasting.Components.BaselineMethods.Autocorrelation_1647968666995.pdf
01.Time-Series-Forecasting_1647968667161.pdf
02.TS-Differencing.Smoothing.MovingAvg.Exponential_1647968667271.pdf
03.TS-ARIMA.Regression_1647968667408.pdf
04.TS-WhiteNoise.Stationarity_1647968667491.pdf
05.A.TS.Regression.Simple_1647968667572.pdf
05.B.TS-MultipleRegression_1647968667837.pdf
06.TS-Forecast.Validation.Evaluation_1647968667918.pdf
01.ProbabilisticTopicalModeling.LDA.State-of-the-art_1647968668207.pdf
02.ProbabilisticTopicalModeling.LDA.long.with_notes_1647968668250.pdf
CSC540_FAQs_1647968668325.pdf