shagunsodhani

A Few Useful Things to Know about Machine Learning

The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.

1. Learning = Representation + Evaluation + Optimization

All machine learning algorithms have three components:

• Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
• Evaluation function tells how good the machine learning model is.
• Optimisation is the method to search for the most optimal learning model.

zupzup

package main

import (
	"flag"
	"fmt"
	"io"
	"log"
	"net"
	"os"
	"os/signal"

codref

/*

Go-Language implementation of an SSH Reverse Tunnel, the equivalent of below SSH command:

   ssh -R 8080:127.0.0.1:8080 operatore@146.148.22.123

which opens a tunnel between the two endpoints and permit to exchange information on this direction:

   server:8080 -----> client:8080

robobe

#!/usr/bin/env python3
import sys
import os
import cv2
import gi
import signal
import threading

gi.require_version('Gst', '1.0')
from gi.repository import Gst, GObject