Brennan  Veum

Brennan Veum

1617458400

The Validate Subsequence Algorithm: A Deep Dive

Like the way love and money make the world go round, algorithms are what makes the technical job interview go round, along with a sprinkle of data structure fundamentals of course. Understanding these two topics and knowing how to solve them quick is what lays out a firm foundation for any programmer to grasp the skill of how to write efficient code better.

Why is this true? There are many answers to why solving algorithms makes you a better developer, but the simplest way I’d like to address this is by stating a truth. The truth is algorithm is just a fancy word for procedure. In other words it’s just a list (sometimes one that has to repeat before ending) of a series of steps a computer follows to perform a task. So if you can code a procedure to get a result, then it makes it easier to apply your patterns in your procedures to coding projects which is what makes it easier to write better code.

Still too complicated? Think of it this way: a cake mix box’s instructions is considered an algorithm.

#javascript

What is GEEK

Buddha Community

The Validate Subsequence Algorithm: A Deep Dive
Hertha  Mayer

Hertha Mayer

1594769515

How to validate mobile phone number in laravel with example

Data validation and sanitization is a very important thing from security point of view for a web application. We can not rely on user’s input. In this article i will let you know how to validate mobile phone number in laravel with some examples.

if we take some user’s information in our application, so usually we take phone number too. And if validation on the mobile number field is not done, a user can put anything in the mobile number field and without genuine phone number, this data would be useless.

Since we know that mobile number can not be an alpha numeric or any alphabates aand also it should be 10 digit number. So here in this examples we will add 10 digit number validation in laravel application.

We will aalso see the uses of regex in the validation of mobile number. So let’s do it with two different way in two examples.

Example 1:

In this first example we will write phone number validation in HomeController where we will processs user’s data.

<?php

namespace App\Http\Controllers;

use Illuminate\Http\Request;
use App\User;

class HomeController extends Controller
{
    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function create()
    {
        return view('createUser');
    }

    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function store(Request $request)
    {
        $request->validate([
                'name' => 'required',
                'phone' => 'required|digits:10',
                'email' => 'required|email|unique:users'
            ]);

        $input = $request->all();
        $user = User::create($input);

        return back()->with('success', 'User created successfully.');
    }
}

Example 2:

In this second example, we will use regex for user’s mobile phone number validation before storing user data in our database. Here, we will write the validation in Homecontroller like below.

<?php

namespace App\Http\Controllers;

use Illuminate\Http\Request;
use App\User;
use Validator;

class HomeController extends Controller
{
    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function create()
    {
        return view('createUser');
    }

    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function store(Request $request)
    {
        $request->validate([
                'name' => 'required',
                'phone' => 'required|regex:/^([0-9\s\-\+\(\)]*)$/|min:10',
                'email' => 'required|email|unique:users'
            ]);

        $input = $request->all();
        $user = User::create($input);

        return back()->with('success', 'User created successfully.');
    }
}

#laravel #laravel phone number validation #laravel phone validation #laravel validation example #mobile phone validation in laravel #phone validation with regex #validate mobile in laravel

Jade Bird

Jade Bird

1666770774

Variables in Python

In this Python tutorial for beginners, we learn about Variables in Python. Variables are containers for storing data values. A Python variable is a symbolic name that is a reference or pointer to an object.

Code in GitHub: https://github.com/AlexTheAnalyst/PythonYouTubeSeries/blob/main/Python%20Basics%20101%20-%20Variables.ipynb 


Creating Variables

Python has no command for declaring a variable.

A variable is created the moment you first assign a value to it.

Example

x = 5
y = "John"
print(x)
print(y)

Variables do not need to be declared with any particular type, and can even change type after they have been set.

Example

x = 4       # x is of type int
x = "Sally" # x is now of type str
print(x)

Casting

If you want to specify the data type of a variable, this can be done with casting.

Example

x = str(3)    # x will be '3'
y = int(3)    # y will be 3
z = float(3)  # z will be 3.0

Get the Type

You can get the data type of a variable with the type() function.

Example

x = 5
y = "John"
print(type(x))
print(type(y))

Single or Double Quotes?

String variables can be declared either by using single or double quotes:

Example

x = "John"
# is the same as
x = 'John'

Case-Sensitive

Variable names are case-sensitive.

Example

This will create two variables:

a = 4
A = "Sally"
#A will not overwrite a

Python Variables: How to Define/Declare String Variable Types

What is a Variable in Python?

A Python variable is a reserved memory location to store values. In other words, a variable in a python program gives data to the computer for processing.

Python Variable Types

Every value in Python has a datatype. Different data types in Python are Numbers, List, Tuple, Strings, Dictionary, etc. Variables in Python can be declared by any name or even alphabets like a, aa, abc, etc.

In this tutorial, we will learn,

  • How to Declare and use a Variable
  • Re-declare a Variable
  • Concatenate Variables
  • Local & Global Variables
  • Delete a variable

How to Declare and use a Variable

Let see an example. We will define variable in Python and declare it as “a” and print it.

a=100 
print (a)

Re-declare a Variable

You can re-declare Python variables even after you have declared once.

Here we have Python declare variable initialized to f=0.

Later, we re-assign the variable f to value “guru99”

Variables in Python

Python 2 Example

# Declare a variable and initialize it
f = 0
print f
# re-declaring the variable works
f = 'guru99'
print f

Python 3 Example

# Declare a variable and initialize it
f = 0
print(f)
# re-declaring the variable works
f = 'guru99'
print(f)

Python String Concatenation and Variable

Let’s see whether you can concatenate different data types like string and number together. For example, we will concatenate “Guru” with the number “99”.

Unlike Java, which concatenates number with string without declaring number as string, while declaring variables in Python requires declaring the number as string otherwise it will show a TypeError

Variables in Python

For the following code, you will get undefined output –

a="Guru"
b = 99
print a+b

Once the integer is declared as string, it can concatenate both “Guru” + str(“99”)= “Guru99” in the output.

a="Guru"
b = 99
print(a+str(b))

Python Variable Types: Local & Global

There are two types of variables in Python, Global variable and Local variable. When you want to use the same variable for rest of your program or module you declare it as a global variable, while if you want to use the variable in a specific function or method, you use a local variable while Python variable declaration.

Let’s understand this Python variable types with the difference between local and global variables in the below program.

  1. Let us define variable in Python where the variable “f” is global in scope and is assigned value 101 which is printed in output
  2. Variable f is again declared in function and assumes local scope. It is assigned value “I am learning Python.” which is printed out as an output. This Python declare variable is different from the global variable “f” defined earlier
  3. Once the function call is over, the local variable f is destroyed. At line 12, when we again, print the value of “f” is it displays the value of global variable f=101

Variables in Python

Python 2 Example

# Declare a variable and initialize it
f = 101
print f
# Global vs. local variables in functions
def someFunction():
# global f
    f = 'I am learning Python'
    print f
someFunction()
print f

Python 3 Example

# Declare a variable and initialize it
f = 101
print(f)
# Global vs. local variables in functions
def someFunction():
# global f
    f = 'I am learning Python'
    print(f)
someFunction()
print(f)

While Python variable declaration using the keyword global, you can reference the global variable inside a function.

  1. Variable “f” is global in scope and is assigned value 101 which is printed in output
  2. Variable f is declared using the keyword global. This is NOT a local variable, but the same global variable declared earlier. Hence when we print its value, the output is 101

We changed the value of “f” inside the function. Once the function call is over, the changed value of the variable “f” persists. At line 12, when we again, print the value of “f” is it displays the value “changing global variable”

Variables in Python

Python 2 Example

f = 101;
print f
# Global vs.local variables in functions
def someFunction():
  global f
  print f
  f = "changing global variable"
someFunction()
print f

Python 3 Example

f = 101;
print(f)
# Global vs.local variables in functions
def someFunction():
  global f
  print(f)
  f = "changing global variable"
someFunction()
print(f)

Delete a variable

You can also delete Python variables using the command del “variable name”.

In the below example of Python delete variable, we deleted variable f, and when we proceed to print it, we get error “variable name is not defined” which means you have deleted the variable.

Variables in Python

Example of Python delete variable or Python clear variable :

f = 11;
print(f)
del f
print(f)

Summary:

  • Variables are referred to “envelop” or “buckets” where information can be maintained and referenced. Like any other programming language Python also uses a variable to store the information.
  • Variables can be declared by any name or even alphabets like a, aa, abc, etc.
  • Variables can be re-declared even after you have declared them for once
  • Python constants can be understood as types of variables that hold the value which can not be changed. Usually Python constants are referenced from other files. Python define constant is declared in a new or separate file which contains functions, modules, etc.
  • Types of variables in Python or Python variable types : Local & Global
  • Declare local variable when you want to use it for current function
  • Declare Global variable when you want to use the same variable for rest of the program

To delete a variable, it uses keyword “del”.


A Beginner’s Guide To Python Variables

A variable is a fundamental concept in any programming language. It is a reserved memory location that stores and manipulates data. This tutorial on Python variables will help you learn more about what they are, the different data types of variables, the rules for naming variables in Python. You will also perform some basic operations on numbers and strings. We’ll use Jupyter Notebook to implement the Python codes.

Variables are entities of a program that holds a value. Here is an example of a variable:

x=100 

In the below diagram, the box holds a value of 100 and is named as x. Therefore, the variable is x, and the data it holds is the value.

xvariable

The data type for a variable is the type of data it holds. 

In the above example, x is holding 100, which is a number, and the data type of x is a number.

In Python, there are three types of numbers: Integer, Float, and Complex.

Integers are numbers without decimal points. Floats are numbers with decimal points. Complex numbers have real parts and imaginary parts.

Another data type that is very different from a number is called a string, which is a collection of characters.

Let’s see a variable with an integer data type:

x=100

To check the data type of x, use the type() function:

type(x)

type-x

Python allows you to assign variables while performing arithmetic operations.

x=654*6734
type(x)

x-int

To display the output of the variable, use the print() function.

print(x) #It gives the product of the two numbers

Now, let’s see an example of a floating-point number:

x=3.14
print(x)

type(x) #Here the type the variable is float

float

Strings are declared within a single or double quote.

x=’Simplilearn’

print(x)

x=” Simplilearn.”

print(x)

type(x)
x-simplilearn

In all of the examples above, we only assigned a single value to the variables. Python has specific data types or objects that hold a collection of values, too. A Python List is one such example.

Here is an example of a list:

x=[14,67,9]

print(x)

type(x)
x-list

You can extract the values from the list using the index position method. In lists, the first element index position starts at zero, the second element at one, the third element at two, and so on.

To extract the first element from the list x:

print(x[0])

print-x

To extract the third element from the list x:

print(x[2])

Lists are mutable objects, which means you can change the values in a list once they are declared.

x[2]=70 #Reassigning the third element in the list to 70

print(x)
print-x-2

Earlier, the elements in the list had [14, 67, 9]. Now, they have [14, 67, 70].

Tuples are a type of Python object that holds a collection of value, which is ordered and immutable. Unlike a list that uses a square bracket, tuples use parentheses.

x=(4,8,6)

print(x)

type(x)
print-x-3

Similar to lists, tuples can also be extracted with the index position method.

print(x[1]) #Give the element present at index 1, i.e. 8

If you want to change any value in a tuple, it will throw an error. Once you have stored the values in a variable for a tuple, it remains the same.

tuple

When we deal with files, we need a variable that points to it, called file pointers. The advantage of having file pointers is that when you need to perform various operations on a file, instead of providing the file’s entire path location or name every time, you can assign it to a particular variable and use that instead.

Here is how you can assign a variable to a file:

x=open(‘C:/Users/Simplilearn/Downloads/JupyterNotebook.ipynb’,’r’) 

type(x)
x-open

Suppose you want to assign values to multiple variables. Instead of having multiple lines of code for each variable, you can assign it in a single line of code.

(x, y, z)=5, 10, 5

xyyz

The following line code results in an error because the number of values assigned doesn’t match with the number of variables declared.

value-error

If you want to assign the same value to multiple variables, use the following syntax:

x=y=z=1

xyz-1

Now, let's look at the various rules for naming a variable.

1. A variable name must begin with a letter of the alphabet or an underscore(_)

Example:

abc=100 #valid syntax

    _abc=100 #valid syntax

    3a=10 #invalid syntax

    @abc=10 #invalid syntax

. The first character can be followed by letters, numbers or underscores.

Example:

a100=100 #valid

    _a984_=100 #valid

    a9967$=100 #invalid

    xyz-2=100 #invalid

Python variable names are case sensitive.

Example:

a100 is different from A100.

    a100=100

  A100=200
print-a

Reserved words cannot be used as variable names.

Example:

break, class, try, continue, while, if

break=10

class=5

try=100
break-ten

Python is more effective and more comfortable to perform when you use arithmetic operations.

The following is an example of adding the values of two variables and storing them in a third variable:

x=20

y=10

result=x+y

print(result)
x-20

Similarly, we can perform subtraction as well.

result=x-y

print(result)

result-x-y

Additionally, to perform multiplication and division, try the following lines of code:

result=x*y

print(result)

result=x/y

print(result)

result-print-result

As you can see, in the case of division, the result is not an integer, but a float value. To get the result of the division in integers, use “//”the integer division.

The division of two numbers gives you the quotient. To get the remainder, use the modulo (%) operator.

modulo

Now that we know how to perform arithmetic operations on numbers let us look at some operations that can be performed on string variables.

var = ‘Simplilearn’

You can extract each character from the variable using the index position. Similar to lists and tuples, the first element position starts at index zero, the second element index at one, and so on.

print(var[0]) #Gives the character at index 0, i.e. S

print(var[4]) #Gives the character at index 4, i.e. l

var-simplilearn

If you want to extract a range of characters from the string variable, you can use a colon (:) and provide the range between the ones you want to receive values from. The last index is always excluded. Therefore, you should always provide one plus the number of characters you want to fetch. 

print(var[0:3]) #This will extract the first three characters from zero, first, and second index.

The same operation can be performed by excluding the starting index.

print(var[:3])

print-sim

The following example prints the values from the fifth location until the end of the string.

print-ilearn

Let’s see what happens when you try to print the following:

print(var[0:20]) #Prints the entire string, although the string does not have 20 characters.

var-simplilearn-print

To print the length of a string, use the len() function.

len(var)

len-var

Let’s see how you can extract characters from two strings and generate a new string.

var1 = “It’s Sunday”

var2 = “Have a great day”

The new string should say, “It’s a great Sunday” and be stored in var3.

var3 = var1[:5] + var2[5:13] + var1[5:]

print(var3)

great-sunday

Get prepared for your next career as a professional Python programmer with the Python Certification Training Course. Click to enroll now!

Conclusion

I hope this blog helped you learn the concepts of Python variables. After reading this blog, you may have learned more about what a variable is, rules for declaring a variable, how to perform arithmetic operations on variables, and how to extract elements from numeric and string variables using the index position.

#python #programming 

Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Gerhard  Brink

Gerhard Brink

1622622360

Data Validation in Excel

Data Validation in Excel

In this tutorial, let’s discuss what data validation is and how it can be implemented in MS-Excel. Let’s start!!!

What Is Data Validation in Excel?

Data Validation is one of the features in MS-Excel which helps in maintaining the consistency of the data in the spreadsheet. It controls the type of data that can enter in the data validated cells.

Data Validation in MS Excel

Now, let’s have a look at how data validation works and how to implement it in the worksheet:

To apply data validation for the cells, then follow the steps.

1: Choose to which all cells the validation of data should work.

2: Click on the DATA tab.

3: Go to the Data Validation option.

4: Choose the drop down option in it and click on the Data Validation.

data validation in Excel

Once you click on the data validation menu from the ribbon, a box appears with the list of data validation criteria, Input message and error message.

Let’s first understand, what is an input message and error message?

Once, the user clicks the cell, the input message appears in a small box near the cell.

If the user violates the condition of that particular cell, then the error message pops up in a box in the spreadsheet.

The advantage of both the messages is that the input and as well as the error message guide the user about how to fill the cells. Both the messages are customizable also.

Let us have a look at how to set it up and how it works with a sample

#ms excel tutorials #circle invalid data in excel #clear validation circles in excel #custom data validation in excel #data validation in excel #limitation in data validation in excel #setting up error message in excel #setting up input message in excel #troubleshooting formulas in excel #validate data in excel

Mikel  Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020