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@flexzuu
flexzuu / transport-loggging.go
Created August 13, 2019 15:46
wrap a transport to log the body
import "github.com/motemen/go-loghttp"
httpClient.Transport = &loghttp.Transport{
Transport: httpClient.Transport,
LogRequest: func(req *http.Request) {
b, _ := httputil.DumpRequestOut(req, true)
log.Printf("out body: %s", string(b))
},
LogResponse: func(resp *http.Response) {
b, _ := httputil.DumpResponse(resp, true)
import React, { useState } from 'react';
import { withStyles } from '@material-ui/core/styles';
import TextField from '@material-ui/core/TextField';
import Button from '@material-ui/core/Button';
import Dialog from '@material-ui/core/Dialog';
import DialogActions from '@material-ui/core/DialogActions';
import DialogContent from '@material-ui/core/DialogContent';
import DialogContentText from '@material-ui/core/DialogContentText';
import DialogTitle from '@material-ui/core/DialogTitle';
import axios from 'axios';
@geffy
geffy / stacking_example.py
Created October 7, 2017 17:33
Stacking example
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 23 23:16:44 2017
@author: Marios Michailidis
This is an example that performs stacking to improve mean squared error
This examples uses 2 bases learners (a linear regression and a random forest)
and linear regression (again) as a meta learner to achieve the best score.
The initial train data are split in 2 halves to commence the stacking.
@bsodmike
bsodmike / README.md
Last active March 13, 2023 05:04
OC Nvidia GTX1070s in Ubuntu 16.04LTS for Ethereum mining

Following mining and findings performed on EVGA GeForce GTX 1070 SC GAMING Black Edition Graphics Card cards.

First run nvidia-xconfig --enable-all-gpus then set about editing the xorg.conf file to correctly set the Coolbits option.

# /etc/X11/xorg.conf
Section "Device"
    Identifier     "Device0"
    Driver         "nvidia"
    VendorName     "NVIDIA Corporation"
@harshavardhana
harshavardhana / nginx-minio-static.md
Last active March 15, 2024 23:41 — forked from koolhead17/gist:4b8dd8d95ec86368634693cf9ad9391c
How to configure static website using Nginx with MinIO ?

How to configure static website using Nginx with MinIO ?

1. Install nginx

2. Install minio

3. Install mc client

4. Create a bucket:

$ mc mb myminio/static
Bucket created successfully ‘myminio/static’.
@coryhouse
coryhouse / package.json
Last active April 15, 2023 15:08
package.json for Building a JS Development Environment on Pluralsight
{
"name": "javascript-development-environment",
"version": "1.0.0",
"description": "JavaScript development environment Pluralsight course by Cory House",
"scripts": {
},
"author": "Cory House",
"license": "MIT",
"dependencies": {
"whatwg-fetch": "1.0.0"
@btroncone
btroncone / ngrxintro.md
Last active February 9, 2024 15:37
A Comprehensive Introduction to @ngrx/store - Companion to Egghead.io Series

Comprehensive Introduction to @ngrx/store

By: @BTroncone

Also check out my lesson @ngrx/store in 10 minutes on egghead.io!

Update: Non-middleware examples have been updated to ngrx/store v2. More coming soon!

Table of Contents

@yrevar
yrevar / imagenet1000_clsidx_to_labels.txt
Last active April 17, 2024 22:50
text: imagenet 1000 class idx to human readable labels (Fox, E., & Guestrin, C. (n.d.). Coursera Machine Learning Specialization.)
{0: 'tench, Tinca tinca',
1: 'goldfish, Carassius auratus',
2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
3: 'tiger shark, Galeocerdo cuvieri',
4: 'hammerhead, hammerhead shark',
5: 'electric ray, crampfish, numbfish, torpedo',
6: 'stingray',
7: 'cock',
8: 'hen',
9: 'ostrich, Struthio camelus',
@andrewkroh
andrewkroh / beats-logstash-tls.md
Last active August 30, 2023 06:40
Using TLS between Beats and Logstash

Using TLS between Beats and Logstash

Beats to Logstash over TLS

The purpose of this document is to help with configuring and troubleshooting using TLS on the connection between Beats and Logstash.

Configuration

You must configure TLS on both the client and server to make this work. This

@baraldilorenzo
baraldilorenzo / readme.md
Created January 16, 2016 12:57
VGG-19 pre-trained model for Keras

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman