Nik georgeblck

## mxnet_loss_linear.jl
using MXNet
import MXNet.mx: _update_single_output, reset!, get

using Distributions

#####################################
# Custom evaluation metric
# It just summarize predictions, because in the case of custom
# loss layer, ANN output equals to loss function itself

## rasterTool.R
library(cdlTools)
library(rgdal)
library(raster)
library(rgeos)


# rasterize in the R 'raster' package is really slow, depending on your needs this may work better
# Author: Jonathan Lisic
# License BSD
polyExtract <- function(x) {

## L3_stats_homework_5_2016.R
## youtube video: https://youtu.be/AJDo9gpkEcg
library("readr")
library("ggplot2")

## read.csv() <- from base R.  DON'T USE!
hw5 <- read_csv("homework_5.csv") # from readr

hw5$A <- factor(hw5$A)
hw5$B <- factor(hw5$B)
hw5$C <- factor(hw5$C)

## gist:356b941a4006a502a67f68213f1a76b5
FROM nvidia/cuda:8.0-cudnn6-devel-ubuntu16.04

ENV PACKAGES_ROOT=/opt

RUN apt-get update && apt-get install -y --no-install-recommends \
    build-essential \
    cmake \
    git \
    wget \
    libatlas-base-dev \

## faster-rcnn-caffe-master.md

      
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              5 stars
            
          
                acmiyaguchi
                / faster-rcnn-caffe-master.md
            
            
              Last active
              March 16, 2020 14:58
            
              
                py-faster-rcnn on Ubuntu 17.10 and Cuda 9.0
              
          
    Instructions


install cuda-9.0 (https://developer.nvidia.com/cuda-toolkit-archive)
install cudnn-7 (https://developer.nvidia.com/cudnn)
install dependencies for caffe

see the caffe installation guide [1] and wiki page [2]


install boost-1.65 or higher (http://www.boost.org/users/history/version_1_65_1.html) [3]
update caffe-fast-rcnn

Easiest way is to use a rebased branch on BVLC/master. See the notes below if you would rather merge the changes yourself.

$ cd $FRCN_ROOT/caffe-fast-rcnn


## svd_memory.py
# .. Memory benchmarks for SciPy's Singular Value Decomposition ..
# .. Author: Fabian Pedregosa <fabian@fseoane.net>

import numpy as np
from scipy.sparse import linalg as splinalg
from scipy import sparse, linalg
import pylab as pl
from memory_profiler import memory_usage

dims = np.arange(500, 1500, 20)

## powerSampleSize.R
# Simulation study for sample size between/within
# got Treat + Sham between subjects
# got Time within subjects

nPerGroup <- 30
nTime     <- 4
muTreat   <- c(37, 32, 20, 15)
muSham    <- c(37, 32, 25, 22)
stdevs    <- c(12, 10, 8, 6)
stdiff    <- 9

## Extract all frames from a 24 fps movie using ffmpeg
ffmpeg -i shame-run.mov -r 24/1 test/output%03d.jpg

## nn.py
# Tiny example of 3-layer nerual network with dropout in 2nd hidden layer
# Output layer is linear with L2 cost (regression model)
# Hidden layer activation is tanh

import numpy as np

n_epochs = 100
n_samples = 100
n_in = 10
n_hidden = 5

## README.md

      
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                wookietreiber
                / README.md
            
            
              Last active
              December 20, 2022 14:00
            
              
                R - Command Line Interface (CLI) and RStudio - template
              
          
    R - Interactive Sessions and Command Line Interface

This is a template for R projects that should work both on the command line as well as in an interactive session.
Template Files

The template is made up of three files:

foo.r
	using MXNet
	import MXNet.mx: _update_single_output, reset!, get

	using Distributions

	#####################################
	# Custom evaluation metric
	# It just summarize predictions, because in the case of custom
	# loss layer, ANN output equals to loss function itself
	library(cdlTools)
	library(rgdal)
	library(raster)
	library(rgeos)


	# rasterize in the R 'raster' package is really slow, depending on your needs this may work better
	# Author: Jonathan Lisic
	# License BSD
	polyExtract <- function(x) {
	## youtube video: https://youtu.be/AJDo9gpkEcg
	library("readr")
	library("ggplot2")

	## read.csv() <- from base R. DON'T USE!
	hw5 <- read_csv("homework_5.csv") # from readr

	hw5$A <- factor(hw5$A)
	hw5$B <- factor(hw5$B)
	hw5$C <- factor(hw5$C)
	FROM nvidia/cuda:8.0-cudnn6-devel-ubuntu16.04

	ENV PACKAGES_ROOT=/opt

	RUN apt-get update && apt-get install -y --no-install-recommends \
	build-essential \
	cmake \
	git \
	wget \
	libatlas-base-dev \
	# .. Memory benchmarks for SciPy's Singular Value Decomposition ..
	# .. Author: Fabian Pedregosa <fabian@fseoane.net>

	import numpy as np
	from scipy.sparse import linalg as splinalg
	from scipy import sparse, linalg
	import pylab as pl
	from memory_profiler import memory_usage

	dims = np.arange(500, 1500, 20)
	# Simulation study for sample size between/within
	# got Treat + Sham between subjects
	# got Time within subjects

	nPerGroup <- 30
	nTime <- 4
	muTreat <- c(37, 32, 20, 15)
	muSham <- c(37, 32, 25, 22)
	stdevs <- c(12, 10, 8, 6)
	stdiff <- 9
	# Tiny example of 3-layer nerual network with dropout in 2nd hidden layer
	# Output layer is linear with L2 cost (regression model)
	# Hidden layer activation is tanh

	import numpy as np

	n_epochs = 100
	n_samples = 100
	n_in = 10
	n_hidden = 5