Канал: tdhock

sea lions at La Jolla Cova in Mar 2024

sea lions at La Jolla Cova in Mar 2024

Efficient line search optimization of penalty functions in supervised changepoint detection

Efficient line search optimization of penalty functions in supervised changepoint detection

Advantages of Animint2 over Tableau and PowerBI

Advantages of Animint2 over Tableau and PowerBI

Install and configure python support in emacs

Install and configure python support in emacs

Download and install GNU emacs

Download and install GNU emacs

Introduction to machine learning and neural networks, with an application to earth system modeling

Introduction to machine learning and neural networks, with an application to earth system modeling

PeakLearner labeling demo

PeakLearner labeling demo

R batchtools on NAU monsoon cluster

R batchtools on NAU monsoon cluster

Labeled Optimal Partitioning

Labeled Optimal Partitioning

Intro to neural networks for image classification using R/keras

Intro to neural networks for image classification using R/keras

Demonstrating overfitting in regression, introduction to R programming

Demonstrating overfitting in regression, introduction to R programming

Introduction to machine learning and selected applications

Introduction to machine learning and selected applications

Replacing std::sort with sort_index from Armadillo C++ library

Replacing std::sort with sort_index from Armadillo C++ library

Compiling and running arma C++ code

Compiling and running arma C++ code

Porting Eigen C++ code to Armadillo

Porting Eigen C++ code to Armadillo

Moving error detection code from Rcpp interface function to C++ sub-routine

Moving error detection code from Rcpp interface function to C++ sub-routine

Stopping for bad inputs to an Rcpp interface function, before calling a C++ computation function

Stopping for bad inputs to an Rcpp interface function, before calling a C++ computation function

Stopping with an error message in R via Rcpp::stop

Stopping with an error message in R via Rcpp::stop

Systematic test running, transpose in Rcpp interface

Systematic test running, transpose in Rcpp interface

Write a test for Rcpp interface function, fix interface bug

Write a test for Rcpp interface function, fix interface bug

Writing and compiling interface functions for C++ functions using Rcpp

Writing and compiling interface functions for C++ functions using Rcpp

Compile C++ code which links to RcppEigen

Compile C++ code which links to RcppEigen

Hello world package with Rcpp

Hello world package with Rcpp

Converting the zip.train data set to arrays

Converting the zip.train data set to arrays

R keras convolutional neural networks on mnist data

R keras convolutional neural networks on mnist data

Max pooling in convolutional neural networks

Max pooling in convolutional neural networks

Illustration of parameter sharing in convolutional neural networks

Illustration of parameter sharing in convolutional neural networks

Sparse connectivity in convolutional layers of a neural network

Sparse connectivity in convolutional layers of a neural network

Convolution operation for neural networks on image data

Convolution operation for neural networks on image data

Definition of softmax function for multi-class classification in machine learning

Definition of softmax function for multi-class classification in machine learning

Download and visualize mnist pixel intensity data and labels

Download and visualize mnist pixel intensity data and labels

Download and visualize zip.train pixel intensity data and labels

Download and visualize zip.train pixel intensity data and labels

More R/keras coding for neural networks with variable number of hidden layers

More R/keras coding for neural networks with variable number of hidden layers

Coding neural networks with variable number of hidden layers

Coding neural networks with variable number of hidden layers

Comparing subtrain/validation loss for neural networks with variable number of hidden layers

Comparing subtrain/validation loss for neural networks with variable number of hidden layers

Fitting neural networks with different numbers of hidden units

Fitting neural networks with different numbers of hidden units

Visualizing validation loss for neural networks with variable number of hidden units

Visualizing validation loss for neural networks with variable number of hidden units

Min subtrain/validation loss vs number of hidden units, keras in R

Min subtrain/validation loss vs number of hidden units, keras in R

Fixing a subtrain/validation split to use with several neural networks

Fixing a subtrain/validation split to use with several neural networks

Visualizing subtrain/validation loss vs number of hidden units, keras in R

Visualizing subtrain/validation loss vs number of hidden units, keras in R

Training a neural network with two hidden units using keras and R

Training a neural network with two hidden units using keras and R

K-fold cross-validation for estimating test error of neural network train in R with keras

K-fold cross-validation for estimating test error of neural network train in R with keras

keras in R: analyzing the test error as a function of the number of splits

keras in R: analyzing the test error as a function of the number of splits

Keras in R: computing mean validation loss over several subtrain/validation splits

Keras in R: computing mean validation loss over several subtrain/validation splits

Computing test error of a neural network learned using keras in R

Computing test error of a neural network learned using keras in R

Keras in R for neural network with one hidden layer, binary classification on spam data

Keras in R for neural network with one hidden layer, binary classification on spam data

R MNIST keras tutorial

R MNIST keras tutorial

Install tensorflow package in R

Install tensorflow package in R

Convert spam data set to arrays for learning via keras in R

Convert spam data set to arrays for learning via keras in R

Computing and visualizing other prediction accuracy/error metrics (ROC, AUC, TPR, FPR, FNR)

Computing and visualizing other prediction accuracy/error metrics (ROC, AUC, TPR, FPR, FNR)

Visualizing prediction error/accuracy metrics using ggplot2

Visualizing prediction error/accuracy metrics using ggplot2

Coding K-fold cross-validation for computing test error/accuracy

Coding K-fold cross-validation for computing test error/accuracy

Compute error rate and confusion matrix of 1-nearest neighbor predictions on one test set

Compute error rate and confusion matrix of 1-nearest neighbor predictions on one test set

Download and read CSV into R as data.table, then convert to matrix/vector for machine learning

Download and read CSV into R as data.table, then convert to matrix/vector for machine learning

nc::capture_first_df for creating new columns in a data frame, regular expressions with nc R package

nc::capture_first_df for creating new columns in a data frame, regular expressions with nc R package

nc::capture_melt_single for wide-to-tall reshaping data into a single output column with regex

nc::capture_melt_single for wide-to-tall reshaping data into a single output column with regex

Reshaping who data using nc::capture_melt_single and regular expressions with type conversion

Reshaping who data using nc::capture_melt_single and regular expressions with type conversion

Parsing text files to data tables using nc::capture_all_str with regular expressions

Parsing text files to data tables using nc::capture_all_str with regular expressions

nc::capture_all_str for parsing text into a data table, regular expressions with nc R package

nc::capture_all_str for parsing text into a data table, regular expressions with nc R package

Basic matching and type conversion with nc::capture_first_vec, regular expressions with nc R package

Basic matching and type conversion with nc::capture_first_vec, regular expressions with nc R package

nc::capture_first_vec with non-capturing optional groups, regular expressions with nc R package

nc::capture_first_vec with non-capturing optional groups, regular expressions with nc R package

nc::capture_melt_multiple for wide-to-tall reshaping data into multiple output columns with regex

nc::capture_melt_multiple for wide-to-tall reshaping data into multiple output columns with regex

Using R and Emacs Speaks Statistics on Monsoon, NAU's HPC cluster

Using R and Emacs Speaks Statistics on Monsoon, NAU's HPC cluster

Debugging R code in Emacs Speaks Statistics tracebug mode

Debugging R code in Emacs Speaks Statistics tracebug mode

Developing R packages in Emacs Speaks Statistics

Developing R packages in Emacs Speaks Statistics

Basic R completion in Emacs Speaks Statistics

Basic R completion in Emacs Speaks Statistics

Configuring tab completion in Emacs Speaks Statistics, interactively setting indentation style

Configuring tab completion in Emacs Speaks Statistics, interactively setting indentation style

Configuring compile-command, useful in Emacs for building R packages with C++ code

Configuring compile-command, useful in Emacs for building R packages with C++ code

Setting default indentation style in Emacs Speaks Statistics, configuring eldoc minibuffer R help

Setting default indentation style in Emacs Speaks Statistics, configuring eldoc minibuffer R help

Starting R and getting R help inside of emacs, basic key command for movement and code execution

Starting R and getting R help inside of emacs, basic key command for movement and code execution

Basic key commands and customizations for emacs

Basic key commands and customizations for emacs

Setting CapsLock as another Ctrl key on windows, for more efficient emacs usage

Setting CapsLock as another Ctrl key on windows, for more efficient emacs usage

First steps with emacs: taking the tutorial

First steps with emacs: taking the tutorial

How to install emacs, Goulet emacs distribution, check emacs version

How to install emacs, Goulet emacs distribution, check emacs version

Vignette building in an R package

Vignette building in an R package

Writing a vignette for an R package

Writing a vignette for an R package

Creating C++ code that uses the Eigen template library in an R package

Creating C++ code that uses the Eigen template library in an R package

Creating C++ code and interface files inside an R package

Creating C++ code and interface files inside an R package

Writing tests for an R package

Writing tests for an R package

Submit an R package to win-builder for standard checking

Submit an R package to win-builder for standard checking

Checking an R package

Checking an R package

Coding R functions and documentation in an R package

Coding R functions and documentation in an R package

Checking your email to see result of R package check on win-builder

Checking your email to see result of R package check on win-builder

Creating a new github repo, commit and push

Creating a new github repo, commit and push

Brian Symbolic Memorial

Brian Symbolic Memorial

SegAnnDB: zooming, annotating, and exporting high density profiles

SegAnnDB: zooming, annotating, and exporting high density profiles

Adding copy number and breakpoint annotations on SegAnnDB

Adding copy number and breakpoint annotations on SegAnnDB