Divyang Panchasara divyang4481

This is work in progress

I read about the .NET Generic Host for the first time in a Tweet of David Fowler. And I was hooked by the idea, it was just what I was looking for.

I like modules making things small but fit together like pieces of a puzzle, if possible being able to reuse them. I started refactoring Greenshot over a year ago, and was making modules out of the spaghetti code that it was. Trying to extract the modules into their own nuget packages, making them more generic and testable. I glued them together with some code I wrote, which is availble in Dapplo.Addons, but I knew that this was just a journey, until I found something that is a better with more potential.

I found the following description: _The purpose of Generic Host is to enable a wider array of host scenarios. Messaging, backg

Storage Tiers

Block Devices
Filesystems
Object Storage
Application Domain

Block Devices

At the bottom we have block devices supplied by HDDs and SSDs. These use a protocol like SCSI or ATA or SAS or SATA.

Population Variance/Stddev vs Sample Variance/Stddev

According to: http://vortex.ihrc.fiu.edu/MET4570/members/Lectures/Lect05/m10divideby_nminus1.pdf the reason why we use n - 1 is because that ensures that the average of all sample variances of every combination of sample (with replacement) of a population is equal to the population variance!

import random
import numpy as np

def calculate_variance(population):

Multi-input and Multi-output Unix Processes

When writing a multi-input and/or multi-output process. Use this style:

process --foo=foo --bar=bar --output-foo=output_foo --output-bar=output_bar

To represent STDIN or STDOUT you can optionally use -.

Stress Testing Tools

iperf - For network bandwidth
owamp - For one-way latency
fio - For filesystem testing
stress-ng - For memory testing, CPU testing
dnsperf - Testing DNS
tc from iproute2 - For simulating network behaviours
tsung - Distributed Application Protocol Load Generator
ostinato - Packet Generator

Convolution and Correlation in Deep Learning, Tensorflow and Theano

Convolution in deep learning works by applying a kernel (a small matrix) to a larger input matrix. You slide this kernel on the input matrix from the top left to the bottom right. You perform element-wise multiplication on each slide (where the sliding distance is the stride length), then you sum all the multplications into a single number. This number is then put in the output matrix.

Strictly speaking, convolution requires the kernel to be flipped horizontally and vertically (transposed). The reason to do this is to acquire the commutative property of convolution. However this is not required in deep learning frameworks or neural network implementations because as a human you don't interpret the kernel.

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	using System;
	using System.Reflection;

	public interface ITest
	{
	void TestFun(int arg);
	}

	public class TestClass : ITest
	{

	version: "3.3"

	services:
	mongo:
	image: mongo:4.1.1
	restart: on-failure
	command: --wiredTigerCacheSizeGB 3
	ports:
	# Charts db is available under port 27018 to not block the default mongo port
	- "8082:8081"

	object obj = new List<int> { 1, 2, 3 };
	TypedReference trObj = __makeref(obj);
	IntPtr ptrObj = (IntPtr)&trObj;