steermomo

import qupath.lib.scripting.QP
import qupath.lib.geom.Point2
import qupath.lib.roi.PolygonROI
import qupath.lib.objects.PathAnnotationObject
import qupath.lib.images.servers.ImageServer


//Aperio Image Scope displays images in a different orientation
def rotated = false

doraneko94

from sklearn.metrics import roc_auc_score
from math import sqrt

def roc_auc_ci(y_true, y_score, positive=1):
    AUC = roc_auc_score(y_true, y_score)
    N1 = sum(y_true == positive)
    N2 = sum(y_true != positive)
    Q1 = AUC / (2 - AUC)
    Q2 = 2*AUC**2 / (1 + AUC)
    SE_AUC = sqrt((AUC*(1 - AUC) + (N1 - 1)*(Q1 - AUC**2) + (N2 - 1)*(Q2 - AUC**2)) / (N1*N2))

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

wangruohui

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

guyskk

[Unit]
Description=Shadowsocks Server
After=network.target

[Service]
ExecStart=/usr/local/bin/ssserver -c /etc/shadowsocks/ss-config.json
Restart=on-abort

[Install]
WantedBy=multi-user.target

hustwj

Notes on Backpropagation

Notations

Let's begin with a notation which lets us refer to weights in the network in an unambiguous way. We'll use $w_{jk}^{l}$ to denote the weight for the connection from the $k^{th}$ neuron in the $(l−1)^{th}$ layer to the $j^{th}$ neuron in the $l^{th}$ layer. So, for example, the diagram the below shows the weight on a connection from the $4^{th}$ neuron in the $2^{nd}$ layer to the $2^{nd}$ neuron in the $3^{rd}$ layer of a network:

awjuliani