DEV Community: Shubham Singh

Mechanics of Neural Network

Shubham Singh — Fri, 10 Jun 2022 09:06:33 +0000

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Neural networks comprise multiple layers and each layer contains a certain number of nodes, each node is connected to each node from previous layer node (except input layer).

The first and the last layer are known as input layer and output layer respectively. Number of nodes in input layer are number of input features which can be represented as a matrix of nxm size where n is number of column and m in number of rows. Output as we can be represented in a matrix.

How ANNs works?

What a neural network do is try to find probability of each node in deep neural network (refer main image) and according to it finding is showing the output.
If you refer to the first image you see, each node is connected to the other node with lines, this line represent an equation.

N = W_{i}a + b

whole network can be written as

Z^{[n]} = W^{[n+1]}A^{[n]} + b^{[n+1]}

Z → inactivated first layer
W → matrix of weights
A → Data
b → bias

This z function is passed through an activation function which help in understanding complex pattern in ANNs.
If you don't use any activation function in your network, it will only understand linear relationships, which will lead to poor quality ANNs.

On each iteration, 2 values get tuned, i.e., W and b. each iteration comprises two type of prorogation forward and backward propagation

On the first iteration, some random weight and bias will be picked, and an activation function will be applied.
When the propagation finishes, cost function will be calculated which help in tuning of the Weights and biases.

Note:
code use here is from my custom neural network from digit datause here is from my custom neural network from digit data

self.W1 = np.random.rand(10, 784) - 0.5
self.b1 = np.random.rand(10, 1) - 0.5
self.W2 = np.random.rand(10, 10) - 0.5
self.b2 = np.random.rand(10, 1) - 0.5

def forward_propragation(self):
    Z1 = self.W1.dot(self.X) + self.b1
    A1 = self.ReLU(Z1)
    Z2 = self.W2.dot(A1) + self.b2
    A2 = self.softmax(Z2)

Cost function

\frac{1}{2m}\Sigma_{i=1}^{m}(\bar{y}-y)^2

def backward_propragation(self, Z1, A1, Z2, A2):
    one_hot_Y = self.one_hot()

    dZ2 = A2 - one_hot_Y
    dW2 = 1 / m * dZ2.dot(A1.T)
    db2 = 1 / m * np.sum(dZ2)

    dZ1 = self.W2.T.dot(dZ2) * self.ReLU_deriv(Z1)
    dW1 = 1 / m * dZ1.dot(self.X.T)
    db1 = 1 / m * np.sum(dZ1)

    return dW1, db1, dW2, db2

This function to be low to have a high accuracy. When we have MSE do iterate back which is called a backward propagation.

By doing partial differentiation of the function, you get new weight and biases

\frac{rE_{total}}{rW_{n}}

to find backward propagation manually

changing the weight and biases

small visualization how a neural net works

in mu custom neural net, I have 1 hidden layer and 10 nodes in it, if you use see the gradient descent function, I am just propagating forward and backward and updating the values

    def gradient_descent(self):
        for i in range(self.iterations):
            Z1, A1, Z2, A2 = self.forward_propragation()
            dW1, db1, dW2, db2 = self.backward_propragation(Z1, A1, Z2, A2)
            W1, b1, W2, b2 = self.update_params(self.W1, self.b1, self.W2, self.b2, dW1, db1, dW2, db2)
            if i % 10 == 0:
                print("ITERATION: ", i)
                print("PREDICTION: ", self.get_predictions(A2))

Full source code : https://github.com/ishubhamsingh2e/neural-network-scratch

Understanding Data For Data Analytics, Data Science, and Machine Learning – Part-5

Shubham Singh — Tue, 24 May 2022 13:17:23 +0000

Things to know beforehand

Random variable: takes values from a sample space; probabilities describe which values and set of values are taken more likely.
Event: set of possible values (outcomes) of a random variable that occurs with a certain probability.
Probability function or probability measure: describes the probability P (X ∈ E) that the event E occurs.
Cumulative distribution function: function evaluating the probability that X will take a value less than or equal to x for a random variable (only for real-valued random variables).

[8]Distribution

A Probability distribution is a mathematical function that, stated in simple terms, can be thought of as providing the probability of occurrence of different possible outcomes in an experiment.

The distributions describe the shape of a batch of numbers that is the meaning of distribution. Suppose the different set of numbers there, you want to show what shape it follows whether it is a bell shaped, we can call it is a normal distribution. If it is forming a rectangular shape, we can call it as a uniform distribution like this that describes the shape of a batch of numbers.

Type of Distributions

there are multiple types of distributions based on it shape and what type of data it represents.

Major distribution to take note of are:

Binomial Distribution
Geometric distribution
Bernoulli distribution
Poisson distribution
student's t
chi-squared
Uniform

Distribution is divided into two parts based on its data type, i.e., continues and discreet

When you have data on continues, you have to find the area under the curve for your required probability, and if data is discreet you have to use
good old summation.

Probability Density Function

In probability theory, a probability density function, or density of a continuous random variable, is a function whose value at any given sample in the sample space can be interpreted as providing a relative likelihood that the value of the random variable would be close to that sample.

Cumulative distribution function

The cumulative distribution function of a real-valued random variable X, or just distribution function of X, evaluated at x, is the probability that X will take a value less than or equal to x.

In simpler terms, at a point of the curve, it represents the probability of that point to all the preceding points.

F_X(x) = P(X\leq x)

Binomial Distribution

The binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own Boolean-valued outcome: success (with probability p) or failure (with probability q = 1 − p).

A single success/failure experiment is also called a Bernoulli trial or Bernoulli experiment, and a sequence of outcomes is called a Bernoulli process; for a single trial, i.e., n = 1, the binomial distribution is a Bernoulli distribution. The binomial distribution is the basis for the popular binomial test of statistical significance.

P_x = \binom{n}{x}P^xq^{n-x}

This distribution show the probability of a particular value on

X-axis : values
Y-axis : respected probability

Uniform Distribution

In uniform distribution, probability of occurrence of each event is equivalent to each other.

Poisson Distribution

the Poisson distribution is use when probability of success is very low.

The Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event.

\frac{\lambda^ke^{-\lambda}}{k!}

where

k is the number of occurrences (k=0,1,2 …)
e is Euler's number (e=2.71828...)
! is the factorial function.

Understanding Data For Data Analytics, Data Science, and Machine Learning – Part-4

Shubham Singh — Wed, 18 May 2022 19:26:14 +0000

Things to know beforehand

sample space : In probability theory, the sample space (also called sample description space or possibility space) of an experiment or random trial is the set of all possible outcomes or results of that experiment.

[6] More about Kurtosis and Skewness

Kurtosis

There are 3 types of Kurtosis

Leptokurtic : when the shape density of data is very high in middle, width is less and height is high.
Mesokurtic : when the shape of date is symmetrical like normal distribution.
Platykurtic : when data is spread out, width of distribution is high, and height is low.

If your kurtosis is highly positive, that may show that your sample is accurate because your central tendency represents your data well.

Note:
Not true for all the conditions.

Skewness

The skewness of the distribution is majored using the mean, median, and mode.
When the mean = median = mode the distribution is symmetrical, if your value of skewness shifts the distribution is no longer symmetrical.

When the skewness is positive, a tail on the right of the distribution will be there.
When the skewness is negative, a tail on the left of the distribution will be there.

[7] Introduction to Probability

Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty. The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes (“heads” and “tails”) are both equally probable; the probability of “heads” equals the probability of “tails”; and since no other outcomes are possible, the probability of either “heads” or “tails” is 1/2 (which could also be written as 0.5 or 50%).

\frac{n_{e}}{N}

n, is an event you are interested in
N, is total number of event

Basic Properties of Probability

The probability of a sure event or certain event is 1.
The probability of an impossible event is 0.
The probability of an event E is a number P(E) such that 0 ≤ P (E) ≤ 1. Probability is always a positive number.
If A and B are 2 events that are mutually exclusive, then P(A⋃B) = P(A) + P(B).
An elementary event is an event having only one outcome. The sum of the probabilities of such events of an experiment is 1.
The sum of probabilities of an event and its complementary event is 1. P(A) + P(A’) = 1.
P(A⋃B) = P(A) + P(B) – P(A⋂B).
P(A⋂B) = P(A) + P(B) – P(A⋃B).
If A1, A2, A3, ………, And are mutually exclusive events, then P (A1 ⋃ A2 ⋃ A3… ⋃ An) = P(A1) + P(A2) + ………. + P (An)

Sets

Set theory is the branch of mathematical logic that studies sets, which can be informally described as collections of objects. Although objects of any kind can be collected into a set, set theory, as a branch of mathematics, is mostly concerned with those that are relevant to mathematics as a whole.

Sets are an easy topic to get started with, lean those
Resources:

Combinatorics

Combinatorics is an area of mathematics primarily concerned with counting, both as a means and an end in obtaining results, and certain properties of finite structures. It is closely related to many other areas of mathematics and has many applications ranging from logic to statistical physics and from evolutionary biology to computer science.

Combination

Combination is a selection of items from a set that has distinct members, such that the order of selection does not matter (unlike permutations). For example, given three fruits, say an apple, an orange, and a pear, there are three combinations of two that can be drawn from this set: an apple and a pear; an apple and an orange; or a pear and an orange. More formally, a k-combination of a set S is a subset of k distinct elements of S. So, two combinations are identical if and only if each combination has the same members. (The arrangement of the members in each set does not matter.) If the set has n elements, the number of k-combinations, denoted as Cnk, is equal to the binomial coefficient.

{\binom {n}{k}}={\frac {n(n-1)\dotsb (n-k+1)}{k(k-1)\dotsb 1}},}{\displaystyle {\binom {n}{k}}={\frac {n(n-1)\dotsb (n-k+1)}{k(k-1)\dotsb 1}}

Permutation

Permutation of a set is, loosely speaking, an arrangement of its members into a sequence or linear order, or if the set is already ordered, a rearrangement of its elements. The word “permutation” also refers to the act or process of changing the linear order of an ordered set.

P{r}=\frac{n !}{(n-r) !}

Conditional Probability

Conditional probability is a measure of the probability of an event occurring, given that another event (by assumption, presumption, assertion, or evidence) has already occurred. This particular method relies on event B occurring with some sort of relationship with another event A. In this event, the event B can be analyzed by a conditionally probability with respect to A. If the event of interest is A and the event B is known or assumed to have occurred, “the conditional probability of A given B”, or “the probability of A under the condition B”, is usually written as P(A|B) or occasionally PB(A). This can also be understood as the fraction of probability B that intersects with

A:{\displaystyle P(A\mid B)={\frac {P(A\cap B)}{P(B)}}},{\displaystyle P(A\mid B)={\frac {P(A\cap B)}{P(B)}}}

Statically Independence

two events are independent if and only if:

P (A ∣ B) = P (A)

for part-5 go here

Understanding Data For Data Analytics, Data Science, and Machine Learning – Part-3

Shubham Singh — Sun, 15 May 2022 12:34:19 +0000

[3] Type Of Data in Statics (Random Variable)

By having different type of random variable in your data, statistical method used in analysis and algorithms used to train will be different.
This type of data can have non-numeric data.

[a] Categorical Data

A categorical variable (also called qualitative variable) is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property.
Examples of categorical variables are race, sex, age group, and educational level.

In this example, image Species in a Categorical variable, but Sepal.Length is a Numerical variable.

To convert any data to a categorical data in R

as.factor(data$col)

[1] Nominal Data

These type of variable doesn't have a particular order, or the order doesn't matter.
Example of Nominal Data are sex, race, group, etc.

[2] Ordinal Data

These type of variable does have a particular order, or the order does matter.
Example of Nominal Data are grades, age, size, height, etc.

[b] Numerical Data

This type of data includes data which have only numbers

In this example, both X and Y axes data is numerical.

Generally, you do not need to convert data to numerical because by default it is numeric.

[1] Discrete Data

In Discrete type of data, data can have any value, but it has to be an integer number.
Example, number of person in room, etc.

[2] Continues Data

This type of data can take any type of value, i.e., integers and fractions
for Example, current temperature, distance, etc.

[4] Moments

The moments of a function are quantitative measures related to the shape of the function's graph. If the function represents mass, then the first moment is the center of the mass, and the second moment is the rotational inertia. If the function is a probability distribution, then the first moment is the expected value, the second central moment is the variance, the third standardized moment is the skewness, and the fourth standardized moment is the kurtosis. The mathematical concept is closely related to the concept of moment in physics.

Raw moments:

Raw moments can be defined as the arithmetic mean of various powers of deviations taken from origin. The rth Raw moment is denoted by μr’, r=1,2,3…. Then the first raw moments are given by

Central Moments:

Central moments can be defined as the arithmetic mean of various powers of deviation taken from the mean of the distribution. The rth central moment is denoted by μr, r=1,2,3….

In general, given n observation x1, x2,……., xn the rth order raw moments (r=0,1,2,…) are defined as follows:

Relation between raw moments and central moments

[5] Kurtosis and Skewness

Kurtosis and Skewness are the 2 value that shows how a distribution looks, i.e., how thin and tall it is and where it has a tail or not respectively.

To calculate kurtosis:

\frac{\mu_{4}}{\sigma^4}

μ4 is 4th central moment
σ is standard deviation

kurtosis(data)
# Kurtosis for above graph 2.422853

To calculate skewness:

\tilde{\mu_{3}} = \frac{\Sigma_{i}^{N}(X_{i} - \overline{X})^3}{(N -1 )* \sigma^3}

skewness(data)
# Skewness for above graph is 0.7824835

For Part-4 go here

Understanding Data For Data Analytics, Data Science, and Machine Learning – Part-2

Shubham Singh — Fri, 13 May 2022 07:51:07 +0000

Things to know beforehand

What is Variability?

It is how much data is spread out.

[1] Central Tendency

[2] Median

When your data is very influenced by the outliers then using median is good choice because it is not effected by outliers
to calculate median sort your data (ascending or descending does not matter) and then find the middle point.

Center point will be different based on whether you n is even or odd

[a] when n is even

When n is even, there are 2 centers

\frac{n}{2} Second~point = \frac{n}{2}+1

[b] when n is odd

For odd n it just

\frac{n}{2}

in R both can be calculated with same function

median()

[3] Mode

In Data Mode is the value which occurs most often in the data
calculating mode is a manual task because you have to count occurrence of each value in the Data.

R doesn't have an inbuilt function for mod, so we can use this function

mode <- function(v) {
   uniqv <- unique(v)
   print(uniqv[which.max(tabulate(match(v, uniqv)))])
}
mode(data)

[2] Major of Spread

Understanding spread of data is very important to understand your data better, 2 sets of data can have same mean but different spread which may lead to low quality estimates.

[1] Range

it is one of the simplistic major of variability, to calculate Range :

R an g e = ma x v a l u e - min v a l u e

diff(range(data))
# or
print(max(data) - min(data))

[2] Inter Quartile Range (IQR) and Whiskers Plot

By dividing your data is 4 equal parts, quartiles are generated each quartile contains 25% of data, i.e.,
1st quartile is 25% of data (25th percentile); 2nd quartile is 50% of data (50th percentile); 3rd quartile is 75% of data (75th percentile); 4th quartile is 100% of data (100th percentile).

I QR = 75 t h p erce n t i l e - 25 t h p erce n t i l e

Box and Whiskers Plot is very useful for 5 point summery and understanding spread and Outliers

library(ggplot2)
data <- iris

ggplot(data) + geom_boxplot(
  mapping = aes(
    x = Sepal.Length,
    y = Species
  )
) + coord_flip()

The five point summary in box plot includes the minimum value, the first quartile, the median, the third quartile, and the maximum value.

Each of these can be looked into the plot below.

Minimum value : start of the vertical line.
First Quartile : start of the box in the middle.
Median : bold horizontal line is the point where median lies.
Third Quartile : end of the box in middle.
Maximum value : end of the vertical line.

And if you are wondering what is that point outside the box in virginica it is an outlier.

To compute outliers mathematically, you need a threshold if any point passes the outliers threshold it is considered as outlier.

o u tl i ers t h res h o l d = 1.5 * I QR

[3] Variance

Variance is the expectation of the squared deviation of a random variable from its population mean or sample mean. Variance is a measure of dispersion, meaning it is a measure of how far a set of numbers is spread out from their average value.

S^2 = \sigma^2 = \frac{\Sigma{}(x_{i}-\overline{x})^2}{n-1}

Why it is S^2 because the sum of xi - x bar can result in zero, so we square it to make it a +ve number.

var(data)

[4] Standard Deviation

the standard deviation is a measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean of the set, while a high standard deviation indicates that the values are spread out over a wider range.
It is quite same as Variance difference is SD unit is same as data, but variance is in unit squared.

\sigma = \sqrt{S^2}

sd(data)

Normal distributions with standard deviations of 5 and 10.

For Part-3 go here

Understanding Data For Data Analytics, Data Science, and Machine Learning – Part-1

Shubham Singh — Thu, 12 May 2022 20:47:26 +0000

All the tech giants rely on Data, in this era is one of the thing which drives the world.
Data makes your outcomes more accurate and reliable, not only in the field of computer science, but also in the other major fields of studies like medicine, archeology, architecture, finance, and many more.

To understand data, you need to understand one basics of static's, algebra, and probability. This includes major of central tendency, spread of data, estimation, chance of occurrence, understanding Shape and Distribution, bias and variances, and much more.

The world of data is very vast, so you need to be patient to understand and try to take one thing at a time.

Things to know beforehand

all the code are written in R-lang

Sample
A sample statistic is a piece of statistical information you get from a handful of items.
A sample is just a part of a population. For example, let’s say your population was every American, and you wanted to find out how much the average person earns. Time and finances stop you from knocking on every door in America, so you choose to ask 1,000 random people. This one thousand people is your sample.
Once you have your sample, you’ll get some kind of statistic. A statistic is really just a piece of information—in this example, average earnings.
Population
In statistics, a population is a set of similar items or events which is of interest for some question or experiment
Outliers
An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal. Before abnormal observations can be singled out, it is necessary to characterize normal observations.

[1] Central Tendency

Studying about the center of data is Central Tendency, there are multiple ways to understand about the center.

Central tendency is defined as “the statistical measure that identifies a single value as representative of an entire distribution.”

[1] Mean

It is a major of center which will be effected by your outliers.

In R there is an input in mean functions for trim, takes value between 0-1 and trim the respective data from top and bottom

[a] Arithmetic mean

Arithmetic mean (or, simply, “mean”) is nothing but the average. It is computed by adding all the values in the data set divided by the number of observations in it. If we have the raw data, mean is given by the formula.

\overline{X}= \frac{\Sigma X}{n}

mean(data)

Advantages

Good representation of data.
Repeated samples drawn from the same population tend to have similar means. The mean is therefore the measure of central tendency that best resists the fluctuation between different samples.

Disadvantages

Sensitive to outliers.
Can not be calculated for nominal or nonnominal ordinal data.

[b] Weighted mean

When every entry of data doesn't have the same level of significance, then a weight $w_{i}$ is attached to the entry to represent its significance.

\frac{\Sigma{}W\overline{X}}{\Sigma{}W}

weighted.mean(data)

[c] Geometric Mean

M is an appropriate measure when values change exponentially and in case of skewed distribution that can be made symmetrical by a log transformation.

\sqrt[n]{x_{1}x_{2}...x_{n}}

exp(mean(log(data)))

[d] Harmonic mean

HM is appropriate in situations where the reciprocals of values are more useful. HM is used when we want to determine the average sample size of a number of groups, each of which has a different sample size.

\frac{1}{\Sigma(\frac{1}{x})}{n}

prod(data)^(1/length(data))

DEGREE OF VARIATION BETWEEN THE MEANS

If all the values in a data set are the same, then all the three means (arithmetic mean, GM, and HM) will be identical. As the variability in the data increases, the difference among these means also increases. The arithmetic mean is always greater than the GM, which in turn is always greater than the HM

For Part-2 go here

OwnCloud Setup for Fedora Based System

Shubham Singh — Mon, 09 May 2022 07:37:31 +0000

[1] Installing Dependence

Apache (HTTP server)

sudo dnf install httpd
sudo systemctl enable httpd.service
sudo systemctl start httpd.service

add HTTP server to firewall exclusion

firewall-cmd --permanent --zone=public --add-service=http
firewall-cmd --permanent --zone=public --add-service=https
firewall-cmd --reload

MariaDB

sudo dnf install mariadb-server
sudo systemctl enable mariadb.service
sudo systemctl start mariadb.service

Secure your database installation

pressing y for every thing is ok

/usr/bin/mysql_secure_installation

 mysql_secure_installation

NOTE: RUNNING ALL PARTS OF THIS SCRIPT IS RECOMMENDED FOR ALL
      MariaDB SERVERS IN PRODUCTION USE!  PLEASE READ EACH STEP
      CAREFULLY!

In order to log into MariaDB to secure it, we'll need the
current password for the root user.  If you've just installed
MariaDB, and you haven't set the root password yet, the password
will be blank, so you should just press enter here.

Enter current password for root (enter for none): **<ENTER>**
OK, successfully used password, moving on...

Setting the root password ensures that nobody can log into
the MariaDB root user without the proper authorization.

Set root password? [Y/n] **<ENTER>**
New password: **Your_Password_Here**
Re-enter new password: **Your_Password_Here**

Password updated successfully!

Reloading privilege tables...
 ... Success!

By default, a MariaDB installation has an anonymous user,
allowing anyone to log into MariaDB without having to have
a user account created for them.  This is intended only for
testing, and to make the installation go a bit smoother.  You
should remove them before moving into a production environment.

Remove anonymous users? [Y/n] **<ENTER>**
 ... Success!

Normally, root should only be allowed to connect from
'localhost'.  This ensures that someone cannot guess at the
root password from the network.

Disallow root login remotely? [Y/n] **<ENTER>**
 ... Success!

By default, MariaDB comes with a database named 'test' that
anyone can access.  This is also intended only for testing, and
should be removed before moving into a production environment.

Remove test database and access to it? [Y/n] **<ENTER>**

 - Dropping test database...
 ... Success!

 - Removing privileges on test database...
 ... Success!

Reloading the privilege tables will ensure that all changes
made so far will take effect immediately.

Reload privilege tables now? [Y/n] **<ENTER>**
 ... Success!

Cleaning up...

All done!  If you've completed all of the above steps, your
MariaDB installation should now be secure.

Thanks for using MariaDB!

Install PHP

make sure to check the required version for your OwnCloud version, can be checker using this php -v

sudo dnf install php php-common php-mysqlnd php-xml php-json php-gd php-mbstring

restart HTTP server

sudo systemctl restart httpd

[2] Get OwnCloud

latest OwnCloud can be found on OwnCloud web page here, copy the tar download link

cd /var/www
wget <link>

I'm using v10.8.0, so my download link is #https://download.owncloud.org/community/owncloud-10.8.0.tar.bz2

extract files and set the required permission

tar xjf owncloud-10.8.0.tar.bz2

chown -R apache.apache owncloud
chmod -R 755 owncloud

remove the .tar file

rm -f owncloud-10.8.0.tar.bz2

[3] Setup For Database

make sure you are login as root

mysql -u root -p

enter your root password, and you should be in the database command line interface

CREATE DATABASE owncloud;
GRANT ALL ON owncloud.* to 'root'@'localhost' IDENTIFIED BY '
<password>';
FLUSH PRIVILEGES;
quit

now you should be able to open up the webpage at localhost/owncloud

to access own cloud from the web, you have to add the domain name or the server public IP in the configuration PHP

path for the configuration file is /var/www/owncloud/config/config.php

under trusted domain add your domain/IP

  'trusted_domains' =>
  array (
    0 => 'localhost',
    1 => 'www.example.com',
    2 => '<your public ip>'
  ),

if the configuration is correct, your OwnCloud should be online at <ip/domin>/owncloud

Flask Web-App Setup Using WSGI and Apache or httpd on Debian and fedora based servers

Shubham Singh — Fri, 06 May 2022 12:31:56 +0000

[1] Installing Dependence

Debian

sudo apt-get install libapache2-mod-wsgi python-dev

pip3 install virtualenv

Fedora

sudo dnf install python-dev python3-pip mod_wsgi

pip3 install virtualenv

[2] Setting up the File System

mkdir /var/www/webapp
mkdir /var/www/webapp/logs
mkdir /var/www/webapp/static
touch app.py && touch webapp.wsgi
cd /var/www/webapp

virtualenv venv
source venv/bin/activate

pip install flask

app.py content

from flask import Flask

app = Flask(__name__)

@app.route('/')
def hello():
    return 'Hello, World!'

if __name__ == "__main__":
    app.run()

webapp.wsgi content

#!/usr/bin/python
import sys
import logging

activate_this = "/var/www/webapp/venv/bin/activate_this.py"
logging.basicConfig(stream=sys.stderr)
sys.path.insert(0, '/var/www/web-app/webapp')

with open(activate_this) as file_:
    exec(file_.read(), dict(__file__=activate_this))

from app import app as application

[3] Setting up httpd

Fedora

In /etc/httpd/conf.d/ create a new config file of any name flaskwebapp.conf

<VirtualHost *:80>

    ServerName <you_server_ip>

    ServerAdmin <email>

    WSGIDaemonProcess <webapp> user=apache group=apache threads=5
    WSGIScriptAlias / /var/www/webapp/webapp.wsgi

    <Directory /var/www/webapp/>
        Order allow,deny
        Allow from all
    </Directory>

    #FOR FLASK STATIC FOLDER

    Alias /static /var/www/web-app/webapp/static

    <Directory /var/www/webapp/static/>
        Order allow,deny
        Allow from all
    </Directory>

</VirtualHost>

Debian

for Debian base system running apache2, create .conf in /etc/apache2/sites-available/FlaskApp

.conf content

<VirtualHost *:80>

    ServerName <you_server_ip>

    ServerAdmin <email>

    WSGIDaemonProcess <webapp> user=apache group=apache threads=5
    WSGIScriptAlias / /var/www/webapp/webapp.wsgi

    <Directory /var/www/webapp/>
        Order allow,deny
        Allow from all
    </Directory>

    #FOR FLASK STATIC FOLDER

    Alias /static /var/www/web-app/webapp/static

    <Directory /var/www/webapp/static/>
        Order allow,deny
        Allow from all
    </Directory>

</VirtualHost>

after creating the .conf run this sudo a2ensite <naem_of_the_conf_without_.conf>

[4] changing context's for Fedora

For project folder

cd /var/www
semanage fcontext -a -t httpd_sys_content_t './webapp(/.*)?'
restorecon -vvRF ./webapp

For the WSGI script

cd /var/www
semanage fcontext -a -t httpd_sys_script_exec_t './webapp/webapp.wsgi'
restorecon -vvRF ./webapp/webapp.wsgi

To check context

ls -ldZ <file_or_dir>

[5] Refresh the server

sudo systemctl stop httpd
sudo systemctl start httpd
sudo systemctl enable httpd

To check the Status

sudo systemctl status httpd

Terminal Setup Guide For Developers

Shubham Singh — Tue, 18 Jan 2022 07:00:31 +0000

The terminal is the most important tool for all developers, it enables a developer to increase productivity.
In this article, I will show you a way to get a beautiful terminal, with a perfect shell prompt in your windows system.

Step 1

Get the terminal of your choice I like to use windows terminal because it is officially supposed by Microsoft as well as it is opensource what else you can ask for nowadays.

There are some good alternatives like alacrity (cross-platform), cmder(window’s), Hyper(cross-platform), iTerm2(MacOS)
If you don’t want tabs, go for alacritty it’s the user you GPU for computational acceleration.

You also have some alternative of alacritty with uses GPU acceleration known as kitty also support image in the terminal by default which is quite a tedious task to get configured in other terminals.

Summary:

In short, is prefer to use windows terminal in windows and of Linux base system I use alacritty and for macOS I recommend you to use itTerm2.
In this article, I am setting up a windows terminal as I like you can you any terminal of your choice.

You can use sites like windowsterminalthemes to get a theme of your choice and in windows terminal, you can set different themes for your different shell’s.
To install them in the windows terminal click on the drop-down arrow and click on setting.

and then in the setting menu click on the Open JSON file with this ⛮ icon it should open a file “setting.json”, here I’m using visual studio code.

Pro tip:

Use it if you are not.

Now go down to the property name of “schemes” and add the theme in there from windowsterminalthemes. dev (if you press the Get theme button it should be copied to your clipboard).

And just select theme from the setting menu

Pro Tip Serious One:

Get a package manager to install software and modules from the command line my recommendation for windows is chocolaty.

Install nerd font from here to get icons in terminal like this:

Step-2

Now let’s get to the shell prompt, now you ask what is a shell prompt?
Shell prompt is the line shown every time after execution of the task, by default it doesn’t show more than the current working directory so let’s customize that.

Here I’m using PowerShell but it works will all the major shell’s
To install starship in your system run PowerShell as an administrator and run this command:

choco install starship

Also, get the nerd font if you haven’t by executing this command:

choco install firanf

if you haven’t installed chocolaty install it using this guide.
After successful installation of both starship and firanf run this command in PowerShell

notepad $PROFILE

it should open a notepad window at the end add this line and save the file

Invoke-Expression (&starship init powershell)

Now if you open your PowerShell it should show a green arrow for your prompt, if that’s not what you wanted you can customize your shell promote however you want but if you want my config just add a file “starship.toml” in your C:\Users{your user}.config folder with this inside.

# Don't print a new line at the start of the prompt
add_newline = false

# Make prompt a single line instead of two lines
[line_break]
disabled = false

# Replace the "❯" symbol in the prompt with "➜"
[character]
success_symbol = "[λ](bold red)" #[➜](bold green)

[username]
style_user = "green bold"
style_root = "green bold"
format = "[$user](bold green)@$hostname"
disabled = false
show_always = true

[rust]
format = "via 🦀[$version](red bold)"

[aws]
symbol = "☁️ "

[conda]
symbol = " "

[dart]
symbol = " "

[directory]
read_only = "🔐 "

[docker_context]
format = "via [🐋 $context](blue bold)"

[elixir]
symbol = " "

[elm]
symbol = " "

[git_branch]
format = " [$symbol$branch]($style) "
style = "bold yellow"
symbol = " "

[golang]
symbol = " "

[hg_branch]
symbol = " "

[java]
symbol = " "

[julia]
symbol = " "

[memory_usage]
symbol = " "

[nim]
symbol = " "

[nix_shell]
symbol = " "

[nodejs]
symbol = " "

[package]
symbol = "📦 "

[perl]
symbol = " "

[php]
symbol = " "

[ruby]
symbol = " "

[swift]
symbol = "ﯣ "


[git_commit]
commit_hash_length = 8
style = "bold white"

[git_status]
conflicted = "⚔️ "
ahead = "🏎️ 💨×${count}"
behind = "🐢×${count}"
diverged = "🔱 🏎️ 💨 ×${ahead_count} 🐢 ×${behind_count}"
untracked = "🛤️ ×${count}"
stashed = "📦 "
modified = "📝×${count}"
staged = "🗃️ ×${count}"
renamed = "📛×${count}"
deleted = "🗑️ ×${count}"
style = "bright-white"
format = "$all_status $ahead_behind"

[hostname]
ssh_only = false
format = "[$hostname]($style) "
trim_at = "-"
style = "bold dimmed white"
disabled = false

[time]
time_format = "%T"
format = "🕙 $time($style) "
style = "bright-white"
disabled = true


# [battery]
# full_symbol = "🔋"
# charging_symbol = "🔌"
# discharging_symbol = "⚡"
# disabled = false

# [[battery.display]]
# threshold = 30
# style = "bold red"

Now you have a shell you wanted 😉

Please Like and Share.

forgive for typos CU's I'm NOOB

JAVASCRIPT BEHIND THE SCENES

Shubham Singh — Fri, 07 Jan 2022 10:00:08 +0000

One of the most popular and well-maintained programming languages of the 21’st century.

JavaScript (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, and is best known as the scripting language for Web pages, but it’s used in many non-browser environments as well. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

JavaScript runs on the client-side of the web, which can design / program how the web pages behave in the occurrence of an event. JavaScript is an easy-to-learn and also powerful scripting language, used for controlling web page behavior.

In a nutshell, JavaScript handles the current web and all websites you see nowadays.

Overview

Almost everyone in the world of technology knows about the V8 Engine as existence, and many people also know that JavaScript is a single-thread language.

in the article, we will go through all the concepts of how JavaScript runs it is also true for another similar programming language.

if you are a noob in JavaScript, this article helps you understand abut JavaScript more so that can write more efficient code to get the most out of the performance and efficacy.

this article tells you all the basic components the help to run your *.js plain text file in the browser:

Engine
Runtime Environment
The Call Stack
concurrence and event loop

The JavaScript Engine

as you know, that JavaScript is an interpreted programing language that means it doesn't compile your code to binary prior to the execution, it compiles it on the fly while reading your code from top to bottom

how your computer knows what to do with the hunk of text you wrote?

So here comes the JavaScript engine. JavaScript engine is a well laid out and engineered program which converts your code to binary.

What a JavaScript engine does is parse your written code into tokens, then convert it into a parse tree which can find any errors in your code. If it doesn't have any error, it gets converted to an equivalent binary and gets executed in your device.

The type of compile used to do this task is called a just-in-time compiler.

Every browse has its own JavaScript engine but the most popular out of them is V8 by google. The v8 engine not only powers google chrome but also Node.js which is The JavaScript runtime

Engine	Browser
V8	Google Chrome and Node.js
Spider Monkey	Mozilla Firefox
JavaScriptCore	Safari
Chakra	Microsoft Edge(legacy)

The engine comprises two major components:

Memory Heap: where the memory allocation happens.
Call Stack: this is where your code is executed

the call stack is the place where your actual code is executed, but you can't achieve that without a memory heap, heap is the place where all your object is stored.

for example variables, functions, and everything else.

The Runtime Environment

JavaScript engine doesn't work in a global or in an isolated environment. Rather, it runs in a separate environment that contains many components like JS engine, web API, call back queue, event table, event loop.

all these components together make JER (JavaScript runtime environment).

this runtime environment is the reason which makes JavaScript-capable of asynchronous calls.

The Call Stack

JavaScript has a single call stack, that is why it can execute a single command at a time.

the call stack is a data structure that works on a FIFO system, which means first in First out, which means the command the caller first will be the first to show the output.

for example:

console.log("1");
console.log("2");
console.log("3");
console.log("4");
console.log("5");

Concurrency and the Event Loop

What happens if you have active calls in Call Stack that take a long time to process?
For example, suppose you want to perform complex JavaScript computation in a browser.

You may ask - why is this a problem?

The problem is that while Call Stack has tasks to perform, the browser cannot do anything else - it is blocked. This means that the browser cannot perform, cannot use any other code, it is simply blocked. And this creates a problem.

And that is not the only problem. When your browser starts processing multiple tasks in Call Stack, we may stop responding for a long time. And many browsers take action by suggesting an error, asking you if you want to close a webpage and in the worst-case senior your browser might stop responding to your system.

more about Concurrency and the Event Loop in later articles so stay tuned.