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poet

Paired Open-Ended Trailblazer (POET) and Enhanced POET

poincare-embeddings

PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"

pokemon_gan

This is the code for "Generating Pokemon with a Generative Adversarial Network" by Siraj Raval on Youtube

polyaxon

A platform for reproducible and scalable machine learning and deep learning on kubernetes

pose-tensorflow

Human Pose estimation with TensorFlow framework

pothole_detection

Pothole Detection Using Computer Vision (Masked-RCNN)

pplm

Plug and Play Language Model implementation. Allows to steer topic and attributes of GPT-2 models.

ppsgan

Privacy Preserving Semi Generative Adversarial Networks

practical-pytorch

PyTorch tutorials demonstrating modern techniques with readable code

practical_rl

A course in reinforcement learning in the wild

practicaldatascience.github.io

practice_notebook

uploading all practice ipynb files

prancer

predict-insurance-charges-with-autoai

Create an end-to-end AI solution that will help predict insurance premium cost with IBM Watson Studio and AutoAI.

predicting-spotify-top-list

CS 210 Sabanci University Group Project SPRING 18/19

predicting_winning_teams

This is the code for "Predicting the Winning Team with Machine Learning" by Siraj Raval on Youtube

prediction_assignment

Predict activity quality from activity monitors ##Synopsis Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, your goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. The goal of this project is to predict the manner in which they did the exercise. This is the classe variable in the training set. Data description The outcome variable is classe, a factor variable with 5 levels. For this data set, participants were asked to perform one set of 10 repetitions of the Unilateral Dumbbell Biceps Curl in 5 different fashions: exactly according to the specification (Class A) throwing the elbows to the front (Class B) lifting the dumbbell only halfway (Class C) lowering the dumbbell only halfway (Class D) throwing the hips to the front (Class E) Initial configuration The initial configuration consists of loading some required packages and initializing some variables. #Data variables training.file <- './data/pml-training.csv' test.cases.file <- './data/pml-testing.csv' training.url <- 'http://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv' test.cases.url <- 'http://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv' #Directories if (!file.exists("data")){ dir.create("data") } if (!file.exists("data/submission")){ dir.create("data/submission") } #R-Packages IscaretInstalled <- require("caret") ## Loading required package: caret ## Loading required package: lattice ## Loading required package: ggplot2 if(!IscaretInstalled){ install.packages("caret") library("caret") } IsrandomForestInstalled <- require("randomForest") ## Loading required package: randomForest ## randomForest 4.6-10 ## Type rfNews() to see new features/changes/bug fixes. if(!IsrandomForestInstalled){ install.packages("randomForest") library("randomForest") } IsRpartInstalled <- require("rpart") ## Loading required package: rpart if(!IsRpartInstalled){ install.packages("rpart") library("rpart") } IsRpartPlotInstalled <- require("rpart.plot") ## Loading required package: rpart.plot if(!IsRpartPlotInstalled){ install.packages("rpart.plot") library("rpart.plot") } # Set seed for reproducability set.seed(9999) Data processing In this section the data is downloaded and processed. Some basic transformations and cleanup will be performed, so that NA values are omitted. Irrelevant columns such as user_name, raw_timestamp_part_1, raw_timestamp_part_2, cvtd_timestamp, new_window, and num_window (columns 1 to 7) will be removed in the subset. The pml-training.csv data is used to devise training and testing sets. The pml-test.csv data is used to predict and answer the 20 questions based on the trained model. # Download data download.file(training.url, training.file) download.file(test.cases.url,test.cases.file ) # Clean data training <-read.csv(training.file, na.strings=c("NA","#DIV/0!", "")) testing <-read.csv(test.cases.file , na.strings=c("NA", "#DIV/0!", "")) training<-training[,colSums(is.na(training)) == 0] testing <-testing[,colSums(is.na(testing)) == 0] # Subset data training <-training[,-c(1:7)] testing <-testing[,-c(1:7)] Cross-validation In this section cross-validation will be performed by splitting the training data in training (75%) and testing (25%) data. subSamples <- createDataPartition(y=training$classe, p=0.75, list=FALSE) subTraining <- training[subSamples, ] subTesting <- training[-subSamples, ] Expected out-of-sample error The expected out-of-sample error will correspond to the quantity: 1-accuracy in the cross-validation data. Accuracy is the proportion of correct classified observation over the total sample in the subTesting data set. Expected accuracy is the expected accuracy in the out-of-sample data set (i.e. original testing data set). Thus, the expected value of the out-of-sample error will correspond to the expected number of missclassified observations/total observations in the Test data set, which is the quantity: 1-accuracy found from the cross-validation data set. Exploratory analysis The variable classe contains 5 levels. The plot of the outcome variable shows the frequency of each levels in the subTraining data. plot(subTraining$classe, col="orange", main="Levels of the variable classe", xlab="classe levels", ylab="Frequency") The plot above shows that Level A is the most frequent classe. D appears to be the least frequent one. Prediction models In this section a decision tree and random forest will be applied to the data. Decision tree # Fit model modFitDT <- rpart(classe ~ ., data=subTraining, method="class") # Perform prediction predictDT <- predict(modFitDT, subTesting, type = "class") # Plot result rpart.plot(modFitDT, main="Classification Tree", extra=102, under=TRUE, faclen=0) Following confusion matrix shows the errors of the prediction algorithm. confusionMatrix(predictDT, subTesting$classe) ## Confusion Matrix and Statistics ## ## Reference ## Prediction A B C D E ## A 1266 208 25 91 29 ## B 33 535 71 30 67 ## C 28 90 676 130 94 ## D 45 72 59 501 43 ## E 23 44 24 52 668 ## ## Overall Statistics ## ## Accuracy : 0.7435 ## 95% CI : (0.731, 0.7557) ## No Information Rate : 0.2845 ## P-Value [Acc > NIR] : < 2.2e-16 ## ## Kappa : 0.6738 ## Mcnemar's Test P-Value : < 2.2e-16 ## ## Statistics by Class: ## ## Class: A Class: B Class: C Class: D Class: E ## Sensitivity 0.9075 0.5638 0.7906 0.6231 0.7414 ## Specificity 0.8994 0.9492 0.9155 0.9466 0.9643 ## Pos Pred Value 0.7820 0.7269 0.6640 0.6958 0.8237 ## Neg Pred Value 0.9607 0.9007 0.9539 0.9276 0.9431 ## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837 ## Detection Rate 0.2582 0.1091 0.1378 0.1022 0.1362 ## Detection Prevalence 0.3301 0.1501 0.2076 0.1468 0.1654 ## Balanced Accuracy 0.9035 0.7565 0.8531 0.7849 0.8528 Random forest # Fit model modFitRF <- randomForest(classe ~ ., data=subTraining, method="class") # Perform prediction predictRF <- predict(modFitRF, subTesting, type = "class") Following confusion matrix shows the errors of the prediction algorithm. confusionMatrix(predictRF, subTesting$classe) ## Confusion Matrix and Statistics ## ## Reference ## Prediction A B C D E ## A 1394 2 0 0 0 ## B 1 946 8 0 0 ## C 0 1 846 6 0 ## D 0 0 1 796 1 ## E 0 0 0 2 900 ## ## Overall Statistics ## ## Accuracy : 0.9955 ## 95% CI : (0.9932, 0.9972) ## No Information Rate : 0.2845 ## P-Value [Acc > NIR] : < 2.2e-16 ## ## Kappa : 0.9943 ## Mcnemar's Test P-Value : NA ## ## Statistics by Class: ## ## Class: A Class: B Class: C Class: D Class: E ## Sensitivity 0.9993 0.9968 0.9895 0.9900 0.9989 ## Specificity 0.9994 0.9977 0.9983 0.9995 0.9995 ## Pos Pred Value 0.9986 0.9906 0.9918 0.9975 0.9978 ## Neg Pred Value 0.9997 0.9992 0.9978 0.9981 0.9998 ## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837 ## Detection Rate 0.2843 0.1929 0.1725 0.1623 0.1835 ## Detection Prevalence 0.2847 0.1947 0.1739 0.1627 0.1839 ## Balanced Accuracy 0.9994 0.9973 0.9939 0.9948 0.9992 Conclusion Result The confusion matrices show, that the Random Forest algorithm performens better than decision trees. The accuracy for the Random Forest model was 0.995 (95% CI: (0.993, 0.997)) compared to 0.739 (95% CI: (0.727, 0.752)) for Decision Tree model. The random Forest model is choosen. Expected out-of-sample error The expected out-of-sample error is estimated at 0.005, or 0.5%. The expected out-of-sample error is calculated as 1 - accuracy for predictions made against the cross-validation set. Our Test data set comprises 20 cases. With an accuracy above 99% on our cross-validation data, we can expect that very few, or none, of the test samples will be missclassified. Submission In this section the files for the project submission are generated using the random forest algorithm on the testing data. # Perform prediction predictSubmission <- predict(modFitRF, testing, type="class") predictSubmission ## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ## B A B A A E D B A A B C B A E E A B B B ## Levels: A B C D E # Write files for submission pml_write_files = function(x){ n = length(x) for(i in 1:n){ filename = paste0("./data/submission/problem_id_",i,".txt") write.table(x[i],file=filename,quote=FALSE,row.names=FALSE,col.names=FALSE) } } pml_write_files(predictSubmission)

pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

probability_analysis_package

Object Oriented Python Data Science Project for Udacity AWS Machine Learning Foundations

product_category

Product cateogry prediction

productionalizing-data-pipelines-airflow

Productionalizing Data Pipelines with Apache Airflow

proglearn_2021-2022

Progress tracking repo for Neuro Data Design class of 2021-2022

progressive_growing_of_gans

Progressive Growing of GANs for Improved Quality, Stability, and Variation

project

project-bodypix

BodyPix model demo application for Google Coral

project-week-3-data-thieves

project-week-8-final-project

project_codenet

This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX

projectoxford-clientsdk

This repo has moved :house: Visit our website for the latest SDKs & Samples

promptsource

Toolkit for collecting and applying prompts