Trending October 2023 # Learn How Typescript Number Data Type Works # Suggested November 2023 # Top 13 Popular |

Trending October 2023 # Learn How Typescript Number Data Type Works # Suggested November 2023 # Top 13 Popular

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Introduction to TypeScript number

TypeScript number is one of the data types which is similar to JavaScript and supports numeric values as number objects. All numbers are stored as floating-point values, which are treated as the number data type. It is used to represent both integers as well as floating-point values. This number type converts numeric literals to an instance of number classes, which acts as a wrapper and manipulates number literals as they are objects. Number data type stores numbers as a double-precision 64-bit number and has two types of number data types which are Primitive number data type and the other is BigInt, a new addition to TypeScript. Let us see how the Number data type is used and solve a few examples.

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No arguments are passed the above syntax, it is just the initialization of number type to an identifier.

For example

let empID: number = 101; let age: number = 25; let price: number = 150.75

Using literal syntax:

 Using number global function:

let identifier_name = Number(101);

In TypeScript, the number data type supports decimal literals, hexadecimal, and floating-point numbers. In addition to these, it also supports binary and octal values that are introduced in ECMAScript 2023.

For example

let code: number = 0x42A; let vaCode: number = 0t455; let bCode: number = 100101;

The above syntax can be decoded as,

[Keyword] [Variable name]: [number] = [value]

[Keyword]: it is the keyword of the variable, like let, var, or const that defines the scope and usage of the variable.

[Variable name]: Also can be any Identifier, name of the variable to hold the value.

[number]: It is the number of data types the variable holds.

[value]: the value that is assigned to the variable

Examples of TypeScript number

Let us see How number data type works, with a few examples:

Example #1

TypeScript number data type

let empID1: number = 101; let empsalary: number = 3.45; let empVC: number = 0x240F; let empCouID: number = 0b0011; let empEncrypt: number = 0o214; console.log(empID1); console.log(empsalary); console.log(empVC); console.log(empCouID); console.log(empEncrypt);


So here we have displayed all kinds of numerical data, octal, decimal literal, hexadecimal and floating numbers are all taking the data type as Number.

TypeScript number has some properties, listed below:

MAX_VALUE: will return the largest possible value of the number in JavaScript, 1.797693134862317E+308

MIN_VALUE: will return the smallest possible value of the number in JavaScript, 5E-324

POSITIVE_INFINITY: will return a value that is greater than MAX_VALUE

NEGATIVE_INFINITY: will return a value that is less than MIN_VALUE

NaN: when some number calculation is not represented by the valid number, it will return NaN which is equal to a value that is not a number.

EPSILON: will return value which is difference between 1 and the smallest value greater than 1

prototype: used to assign newer properties and methods to Number type object

parseInt: used to convert string to integer

parseFloat: used to convert string to a floating-point integer.

Example #2 let empid = 0; if (empid <= 0) { console.log('Not a number', Number.NaN); } else { console.log('number', empid); } console.log('Max value', Number.MAX_VALUE); console.log('Min value', Number.MIN_VALUE); console.log('Positive infinity value', Number.POSITIVE_INFINITY); console.log('Negative infinity value', Number.NEGATIVE_INFINITY); console.log('Epsilon value', Number.EPSILON); console.log('prototype', Number.prototype);


Here, we have looked for all the Number properties.

TypeScript Number Method:

toFixed(): returns the fixed point notation in string format


toExponential(): returns exponential notation in string format

number.toExponential([count of fraction digits])

toString(): returns the string representation of the given number in specified base value


toPrecision(): returns string representation in fixed point or exponential in specified precision.


toLocaleString(): converts number to a local specified representation of the number.

number.toLocaleString([locales, [, optionl]]) number.valueOf() Example #3

TypeScript number method

let price: number = 120.457; console.log(price.toFixed()); console.log(price.toFixed(1)); console.log(price.toFixed(2)); console.log(price.toFixed(3)); let exp: number = 454280; console.log(exp.toExponential()); console.log(exp.toExponential(1)); console.log(exp.toExponential(2)); console.log(exp.toExponential(3)); let nameID: number = 368; console.log(nameID.toString()); console.log(nameID.toString(2)); console.log(nameID.toString(4)); console.log(nameID.toString(8)); console.log(nameID.toString(16)); console.log(nameID.toString(36)); let precID: number = 12.432; console.log(precID.toPrecision(1)); console.log(precID.toPrecision(2)); console.log(precID.toPrecision(3)); let localeId: number = 21778.435; console.log(localeId.toLocaleString()); console.log(localeId.toLocaleString('de-DE')); console.log(localeId.toLocaleString('ar-EG')); let valId = new Number(563); console.log(valId) console.log(valId.valueOf()) console.log(typeof valId) let valIdx = valId.valueOf() console.log(valIdx) console.log(typeof valIdx)


So here we have executed all the numerical methods.

Big Integers: it represents the whole numbers greater than 253 – 1. Big integer has n character at the end of an integer literal, as,

bigint = 90403900382042442342n;

number vs Number:

The number is a primitive data type whereas the Number is a wrapper object around the number type. Users can assign a primitive number to the Number object, but not vice versa.


With this, we conclude the topic. We have seen what is a number data type and how is it used. Looked into the initialization of number identifiers or variables, and their methods and properties. We have also worked on some of the examples depicting the values of numerical methods and properties. All numbers are either big integers that take bigint type or floating-point numbers that take number data type. Do not use ‘Number’ in TypeScript as it is a non-primitive object. Thanks! Happy Learning!!

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Learn How To Run And Fit Data With Keras?

Introduction to Keras fit

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What is Keras’s fit?

Keras fit is one of the APIs used to train the model. In contrast, the model is being trained repetitively for the specific number of iterations or epochs specified for the mentioned dataset.

How to Use Keras fit?

We can use Keras fit function by following the below syntax of a function and passing the necessary values or parameters according to our necessity. For example, the below statement – value, y value, epochs = 100, batch_size = 32)

When the call to the fit function of Keras is made, certain assumptions are made prior, which are specified below –

The complete set of training data can be accommodated in the provided RAM of the system, Random Access Memory, as the fit function is only used when there is no data augmentation.

The already trained weights will not be reinitialized when given the call to fit function a second time or again. We can even carry out consecutive calls for the Keras fit function but manage the same on our level.

As no data augmentation will occur, we will not use any Keras generators.

We will train the model and Keras network using the raw data itself, and the same will be fitted inside our memory, that is, RAM.

How to run and fit data with Keras?

While running and fitting our Keras model, we need to follow certain steps mentioned below –

Required libraries should be imported at the top of the program, which may include pandas, NumPy, sampleEducba, which is nothing but our training dataset(you can select your dataset that is to be used for training), datasets, model_selection, Keras models, layers such as dense, dropout and sequential.

Dataset should be loaded – We will be using the x_sample_educba_data and y_sample_educba_data along with our imported sampleEducba dataset as shown below by using this statement – (x_sample_educba_data, y_sample_educba_data), (x_test_educba_data, y_test_educba_data) = sampleEdcuab.load_data()

The next steps will be to create the model and add the required layers to our model.

After creating the model, we will compile the same using the compile method function.

To fit our model, we will be simply using a healthy function. Say for example,, y_sample_educba_data), batch_size = 32, epochs = 5, verbose = 1, validation_data = (x_test_educba_data, y_test_educba_data)

Keras fit function

sampleEducbaModel. Fit (x = None, y = None, batch_size = None, epochs = 1 = verbose = “auto”, callbacks = None, validation_split = 0.0, validation_data = None, shuffle = True, class_weight = None, sample_weight = None, initial_epoch = 0, steps_per_epoch = None, validation_steps = None, validation_freq =1, max_queue_size = 10, workers = 1, use_multiprocesing = False)

Various arguments used in the above syntax are explained below –

X – The data is passed as input and can have the value of NumPy array or Numpy arraylike, TensorFlow tensor value, dict mapping names of input or list of tensors. A numpy array is specified when we need to pass multiple inputs to the model. A tensor or tensor list is also used in the same scenario. In contrast, dict mapping is used for specifying the names of the inputs corresponding to the tensors or arrays provided if the model contains the inputs named ones.

Y – It is the value of the data that is targeted. Even this value can be a Numpy array, tensor, or list of tensors, but the condition is that the value should be consistent with the x parameter. So, for example, we cannot have tensor inputs and NumPy targets or vice versa.

Batch_size – It has the value set to either None or integer and is used to specify sample count per update of a gradient. When not specified, the default value is considered as 32. When the input data is going to create generators of data sets, there is no need to mention the batch size.

Epochs – It is an integer number where we specify the epochs we must carry out to train the model. Epoch consists of an iteration for x and y data that are mentioned.

Verbose – It can have the value of 0, 1, 2, or auto where one is for the progress, 0 for silent, and auto is the default value set to 1, 2 is the value that specifies one line to be considered per epoch or iteration.

Callbacks – It is the list of the instances of callbacks that are implemented during training.

Validation_split – It has the float value and can be either 1 or 0, which is the specification of a fraction of training data that will be used for validation.

Validation_data – It is the data that will be considered for the loss evaluation and metrics for the model.

Class_weight is the optional parameter for mapping dictionary indices of class with the corresponding float or weight values that are used further for calculating the loss function.

Steps_per_epoch – It helps in mentioning the count of the steps that need to be used while jumping from one epoch completion to the beginning of the new epoch. When not mentioned, the default value is treated as Null.


Keras fit model is used for training the Keras model by passing the required training data and iterating it for a necessary number of times as specified by epoch, which aims to update the mathematical variables present internally by the model.

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Why Do We Need To Learn Data Science?

Data Science Tutorial and Resources

We are hearing a lot about data nowadays since the internet has become an ever-increasing knowledge plate form. Nowadays, a particular individual generates TeraBytes of data in a week due to their social network commitments and other internet usage. This is the best time to be a data scientist since, through that particular data, one can draw multiple insights from credit card sales to mobile data sales, health predictions and weather forecasting, and so on. Data drive every application on the mobile or the internet we are using. All the major companies are hugely investing in data science to make themselves future-ready.

Why do we need to learn data science?

As everything around us is completely driven by the data we are only generating, we are leaving a footprint of us in the form of data while browsing the internet or surfing around mobile apps. So to capture and use the huge potential of the data, one should learn about this field, as this is the future. Data science is not only a field that is bifurcated from the field of computer science. Its an amalgamation of various Fields, such as in the below figure.

Data science is the intersection of 3 Fields that are:

1. statistics: This plays a vital role since mathematics is the crux of data science.

2. Data analysis: This is also very important as the data needs to be analyzed and plotted to identify its intricacies.

3. Machine learning: This comprises the various algorithms involving statistics.

Also, the domain knowledge is very much important(for example one is working on credit card fraud detection, then banking domain knowledge is a must in this scenario)


There is the various application of data science, such as:

 Credit card fraud detection

Recommendation engines

Internet search

Speech recognition

Airline Route Planning

Weather Forecasting

Sales Forecasting

Expenditure Forecasting

Augmented reality


A simple example of a data science application can be sales forecasting:

For example, consider a beverage company (ABBeverage) that wants to launch a special offer in the new year for its users.

That beverage company is 12 years old and has its sales data for 12 years.

So the beverage company will hire a data scientist and ask them to analyze their 12 years of sales data and predict which brand they can provide a discount on and which brand they cannot.

So the data scientist analyzed their sales data for each brand and then told them to give a discount on the x brand, not a discount on the y brand. Since x brand beverage sold the most during the new year and y brand didn’t. But y brand was there a most famous brand of beverage.

Here the data scientist analyzed not only the sales of each brand of beverage but also kept in mind the time of the sale that was(new year)

This is the basic use case of a data science project.


Before starting this tutorial, one should have a basic knowledge of coding, preferably python, and know how the python code is executed in a particular IDE or basic knowledge of a code editor.

Target Audience

This tutorial targets software professionals and software engineering graduates or any other individual who has basic programming knowledge and wants to learn and make a career for himself in the field of data science.

How Css Supports() Function Works

Introduction to CSS.supports()

The following article provides an outline for CSS.supports(). CSS @supports is defined as a support condition, also known as Feature Query, that helps to check browser support for CSS Property value and is a part of CSS3 Conditional Rules Specification used in the design work process. The condition we test should be placed inside the parenthesis; the valid code would be like if we try to use more parenthesis. The operator AND, OR helps to chain the detection of different features.

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Syntax of CSS.supports()

The CSS supports syntax is given as:

@supports (condition) { .ex { } }

Refer to the below table for the value which is defined.

chúng tôi  Value Description

1 AND Value pair combine with conjunction

2 OR It’s a disjunction

3 NOT It’s a negation

How CSS supports() Function works?

CSS support function returns a Boolean value (writing a conditional statement) specifying whether a browser supports CSS features or not. We can also perform multiple checks on the support system using AND, OR. These rules can be nested, which makes code easier while using complex queries. Ideally, Browser support for the CSS3 version is variable to have good practice on it. The detection is performed using JavaScript. To make this done with CSS Style @support function has been come in.

So, this function works well for detection even though JavaScript is disabled. Thereby when we write code, they are very well familiar with a media query.

Let’s see their working with Operators.

1. NOT Keyword

Just like checking browser support, we can also check whether the feature is supported using the operator NOT.

The sample looks like this:

@supports not (display: flex) { .aaa { Display: grid; } }

When a NOT is combined with other operators, enclosing the NOT keyword between two parentheses is unnecessary, such that parenthesis is mandatory when it is the first condition.

Let’s take an example:


@supports not (display: flex) and ((display: grid) or (display: table)) { }


@supports display: flex) or (display: table) not (display: table) { } 2. AND Keyword

This is used to check two conditions, and if both conditions are evaluated to be true, then the style statement is executed. In other terms, they are helpful for multiple required conditions.

3. OR Keyword

Disjunction type and used for multiple alternative styles. We can also use AND and OR together for testing the conditions.

Examples of CSS.supports()

Given below are examples of CSS.supports(). Below are the different scenarios where support is implemented.

Example #1

Showing NOT operator


* { box-sizing: border-box; font-family: Algeria, sans-serif; } .main { max-width: 80%; height: auto; background-color: #084F66; margin: 0 auto; padding: 0; } .city { margin: 0; padding: 0; background: linear-gradient(rgb(12, 185, 242), rgb(6, 49, 64)); } img { display: inline; width: 90%; height: auto; } @supports(mix-blend-mode: saturation) { .city img { mix-blend-mode: overlay; } } @supports not (mix-blend-mode: saturation) { .city img { opacity: 0.7; } } .aa { text-align: center; padding-top: 52px; font-size: 10px; }

Example #2

Implementation with AND Operator


@supports (display: grid) and (display: -webkit-flex) { div h1 { display: -moz-flex; justify-content: -moz-flex-start; color: purple; border: 6px solid purple; padding: 18px; font-size: 30px; } }


Example #3

Using AND operator in support of border Style


@supports (border-radius: 4px) and (box-shadow: 2px 2px 3px blue) { div h1 { border-radius:4px; box-shadow: 2px 2px 3px blue justify-content: flex-start; color: red; border: 6px solid yellow; padding: 15px; font-size: 30px; } }

Above code talks about testing Multiple properties. Here we have tested border-radius and shadow. Therefore it returns true if all of them are met.


Example #4

Support with CSS variables


section { color: green; } @supports(–css: variables) { section { –my-color: red; color: var(–my-color, ‘pink’); } } h1 { text-align: center; max-width: 500px; margin: 40px auto; }

The above code selects the section part to display the text content with a color in the browser.


Example #5

Using OR @supports function


@supports (display:grid) and (transform:rotate(20deg)){ p{ font-size: 2rem; color: orange; } } @supports (not (display:rainbow)) or (display:block){ p{ font-size: 3rem; text-shadow: 4px 4px 4px blue; } }



In this article, we have seen how to create a support function in CSS. Therefore, we conclude that CSS is a pretty good innovation. If something is not supported in the web browser, the nature of CSS is, it will simply ignore the page. Also, we have seen how to use the operators in the support system. @support function is an excellent addition to CSS Specification. Depending on the projects, we will use this rule more and better.

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How Does Tensorflow Transpose Works?

Introduction to TensorFlow Transpose

Tensorflow transpose is the method or function available in the Tensorflow library or package of the Python Machine Learning domain. Whenever we pass the input to the tensorflow model, this function helps us evaluate the transpose of the provided input. In this article, we will have a detailed look at the tensorflow transpose function, how it works, the parameters needed to pass to it, the use of the transpose function, and also have a look at its implementation along with the help of an example. Lastly, we will conclude our statement.

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What is TensorFlow transpose?

The location of the tensorflow transpose function is found in tensorflow/python/ops/ In tensorflow, the transpose function can be used by using the below-mentioned syntax –

sampleTF.transpose ( sampleValue, perm = None , name = ‘transpose’ , conjugate = False)


In the above syntax, the terminologies involved will be described in the below list –

sampleValue – It is the input value that is to be transposed. In case of the value of the conjugate is set to true, then the sampleValue dtype can have the value of either complex128 or complex64, and the sampleValue is the transposed and conjugated value.

Perm – It is the parameter that is responsible for allowing the dimensions of the input.

Returned Output – The output of the transpose function will be the matrix as per the dimensions permitted by perm[n] and corresponding to the dimensions of the input matrix. By default, the perm value is considered to be (m-1….0) when not specified. Here the value of the m is nothing but the rank of the matrix of tensorflow provided in the input. The default transpose operations are carried out on a 2-dimensional input tensor provided.

How does tensorflow transpose works?

The working of transpose is similar to the flipping of the row and column values in a diagonal manner. Let us consider one sample input matrix –

[30, 31, 32]

[ 21, 22, 23],[ 24, 25, 26],[ 27, 28, 29],[30, 31, 32]

Will be transposed to –

[ 23, 26, 29, 32]

[ 21, 24, 27, 30],[ 22, 25, 28, 31],[ 23, 26, 29, 32]

We can observe that the rows and columns are interchanged.

Tensorflow Transpose Function

Tensorflow transpose function allows you to flip the tensor matrix passed as the input. This function is defined inside the file whose directory is inside the path tensorflow/python/ops/ If you pass a matrix that contains the dimension [m, n] where m and are the number of rows and columns, respectively. Then the transpose function will flip the tensor’s input matrix, leading to the interchange of rows and columns by flipping them through a diagonal. The output matrix will be diagonal [n, m].

The transpose function can be called by using the syntax –

sampleTF.transpose(sampleValue, perm = None, name = ‘transpose’, conjugate = False)

TensorFlow Transpose Examples

Let us understand how the transpose of the input tensor works considering one sample matrix of tensorflow for input. Suppose that we call the transpose function of tensorflow by using the below statement –

sampleTFObject. Transpose (sample, perm = [1,0])

The output of either of the above two statements will lead to the conversion of the sample matrix to the following where the rows and columns are interchanged –

[[11,14], [12, 15], [13, 16]]

Let us consider one example where our input matrix will be a complex matrix involving imaginary numbers. If we set the property of conjugate to true, then it will provide us the transpose of the input matrix –

sampleTFObject. Transpose (sample, conjugate = True)

# [13 – 13j, 16 – 16j]]

Let us consider one more example where we will use perm, which helps specify the permutation of the dimensions of the tensor matrix.

[30, 31, 32]]])

We have taken a matrix of 0 dimensions which is the shorthand of ‘linalg.transpose’.

Perm value will be [0, 2, 1] [29, 32]]]

The compatibility of NumPy transpose with TensorFlow transpose is not supporting the stride functionality. The numpy transpose is the efficient operation for memory and has constant time as it gives the same output with the new view of the passed data. It just adjusts the strides in the output view.


The transpose function of tensorflow helps flip the input tensor, leading to the matrix’s interchange of rows and columns.

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How Db2 Insert Works With Examples?

Introduction to DB2 INSERT

DB2 INSERT statement helps us to insert the rows or values of a column in a particular table or view that is present in database while using DB2 RDBMS. Maintaining the database requires storing a lot of values in the database. Most of the times, the data is stored in the format of rows and columns in table. In DB2 relational database, data is stored in tables and we can insert the new row values in the tables by using the INSERT statement. We can also use the INSERT statement to add multiple rows in a single query statement in DB2. Further, we can even insert the data of a particular table into some other table by using SELECT and INSERT statements together.

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In order to insert the values into a particular table, it is necessary that the user should have the insert privileges for that particular table. If we try to insert the row without specifying the value for a certain column, then DB2 will directly internally follow the following rules to give the value to the unspecified columns. If none of the rule is applicable, then an error is thrown in the output –

If the unspecified value column is an identity column then an auto-incremented value is stored in it.

If default value is specified in the definition of that column of the table then the default value is inserted when not specified in INSERT statement.

If the column has the NULLABLE attribute set to true then NULL value is inserted for unspecified value of that column.

If it is a generated column, then the computed value is inserted if a value is not specified while inserting the row using INSERT statement.

We can insert the data for a single row value using a single INSERT statement and also multiple rows using a single query of INSERT statement accompanied with SELECT statement which gets the data of some other table and inserts in the current table. The bulk insertion of the data into the database can also be done by using the LOAD utility of DB2, creating an application program which can insert the values into DB2 database, copying the data of one table to another. Using the INSERT FOR n (number of rows) ROWS statement can be done for mass insertion of records into the table which can be further accompanied by using host variable arrays.

We can add NULL values, host variables, constants, or default values too into columns while inserting the data using INSERT statement. We can also insert row values in a view. When the data is inserted in the view automatically the corresponding tables from which the view is created are also updated and rows are inserted there too. The order in which we specify the names of the columns in the insert statement should match and correspond to the list of the values that we are trying to insert in the table by using our INSERT statement. Also, note that it is necessary to specify all the columns of the table in the INSERT statement’s column list place or else it will throw an error.


The following is the syntax for using the INSERT statement in DB2 to insert the row values into a particular table –

INSERT INTO name of the table [(list of the column names of that table)] VALUES (list of the column values of that table)

In the above syntax, the list of column names of the table is the comma-separated names of the columns of table which are enclosed in simple round parenthesis which can be specified in any order while list of column values are the values that are comma-separated and enclosed in parenthesis that are specified in the same order in which the column names are written and which are to be inserted in the database. It is optional to specify the list of the column names of the table. However, if mentioned then the order should match with the order in which we have specified the values to be inserted. In case, if the column name list is not specified the default order is the order in which the columns are defined in the definition of the table and same order is referred while inserting the column values too.

The syntax of the INSERT statement as provided by the IBM for DB2 RDBMS is as shown in the below image –


Let us consider the table named Sales is created by using the following query statement –

Let us insert the rows in the table sales. We will insert first row by using following statement –


The execution of above query will give following output:

As product_id is auto-generated we don’t need to mention that value while inserting a system will automatically detect the latest value and assign the unique incremented value to that column. If we don’t provide details it will automatically take the NULL value for it as it ha VARCHAR datatype and no NOT NULL constraint. The sold_date column is of TIMESTAMP data type hence it will take current date and time as the default timestamp value. We need to mention product_name compulsorily as it has NOT NULL constraint. We can retrieve the contents of the table by using the SELECT statement in the following way –


Which turns out to give following output:

We will now insert the product_name and details column values using following insert statement –

VALUES(‘Shampoo’, ‘Hair Wash for intense repair and anti-dandruff treatment’);

The execution of above query will give following output:

Let us retrieve all the values using the same select statement and check the results which are as follows –


We can make the use of INSERT statement in DB2 to insert the records in the table. We should keep in mind certain restrictions and usage syntax while doing so which are specified above.

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