DEV Community: Kavindu Lakmal Wickramasinghe

Unlock Free AWS Certification Vouchers Through AWS Educate!

Kavindu Lakmal Wickramasinghe — Mon, 03 Mar 2025 11:11:42 +0000

AWS is offering 100% free vouchers for its Foundational and Associate-level certifications via the AWS Educate Emerging Talent Community (ETC). If you're preparing for an AWS certification, this is your chance to get certified without any cost! Here's everything you need to know to claim your free voucher.

Available Vouchers
Foundational Level (4,500 points required)
Covers:

AWS Certified Cloud Practitioner (CLF-C01)
AWS Certified AI Practitioner
Expires: August 31, 2025

Associate Level (5,200 points required)
Covers:

AWS Solutions Architect Associate
Developer Associate
SysOps Administrator Associate
Data Engineer Associate
Machine Learning Engineer Associate
Expires: August 31, 2025

How to Qualify for the AWS Emerging Talent Community (ETC)?
To join the ETC and access these free vouchers, you must:

Be 18 years or older.
Earn a qualifying badge from an AWS Training program (e.g., AWS Educate, AWS re/Start, or AWS Academy).
Complete at least one badge course in your AWS Educate learning dashboard.

Once you earn a qualifying badge, you’ll gain access to the Emerging Talent Community (ETC), where you can start earning points to claim your free voucher.

Firstly must complete the badge courses(at least one)in your learning dashboard, after you are eligible to ETL community. in this the blue point navigated are courses and there's more below and the red point navigated is the page to go to ETL home page

I already have an AWS Educate Account. How do I access the AWS Emerging Talent Community?

Learners who have qualified for the ETC, can access the ETC by logging into AWS Educate and selecting the “Emerging Talent Community” tab from the navigation bar on the top right of the screen next to your AWS Educate Profile. Please note that it may take up to 72 hours for the “Emerging Talent Community” to appear in your navigation bar if your qualifying badge is not an AWS Educate badge. Learners who earn a qualifying badge in AWS Educate should be able to access the Emerging Talent Community (ETC) within a few minutes of earning their badge if they meet all other ETC eligibility criteria. From the AWS Emerging Talent Community homepage you can complete the AWS Emerging Talent Community onboarding content to get full access to content within the AWS Emerging Talent Community.

How much time does it take for the reward request to be reviewed?
Typically, rewards are reviewed within 3 business days.

How to Earn Points in the ETC?
You can earn points by:

Reading articles
Watching videos
Taking quizzes and surveys
Completing tasks on the ETC homepage Points are displayed in the top right corner of the AWS Educate navigation bar (indicated by a gem icon). You can also check your points and rewards history on the Activity page.

How to Claim Your Free Voucher?

Sign up for AWS Educate (if you don’t already have an account).
Log in and navigate to the Emerging Talent Community (ETC).
Complete tasks to earn 4,500 points (Foundational) or 5,200 points (Associate).
Go to the Rewards tab and request your voucher.

Eligibility Requirements
To claim your free voucher, you must:

Complete an AWS Skill Builder exam prep course for your chosen certification.
Score 80% or higher on an official AWS Skill Builder practice exam or question set.

Important: Ensure your AWS Skill Builder email matches your AWS Educate email to avoid issues.

click here after joining to ETL and earned points

Click here signup skillbuilder.
complete this skillbuilder for foundational badge assessment to get score 80% or high and eligible for requesting foundational exam vouchers by earned points in ETL community.
complete this skillbuilder for associate badge assessment to get score 80% or high and eligible for requesting associate exam vouchers by earned points in ETL community.

Key Notes

New content is added to the ETC 6 times per week, so check frequently for new opportunities.
Rewards are subject to availability and may change over time.
If you’re under 18, you can still participate in AWS Educate courses and earn badges, but you won’t have access to the ETC until you meet the age requirement.

Don’t Miss This Opportunity!

AWS certifications are highly valued in the tech industry, and this is your chance to earn one for free. Act fast, as vouchers are available only while supplies last.

For more details, visit the official AWS Educate FAQ:
AWS Educate Learner FAQs

Start your journey today and get AWS certified for FREE! 🚀

Thank You for Reading!

I hope this information inspires you to take the next step in your career journey. Don’t miss out on this incredible opportunity to earn certifications for free and unlock new possibilities.

Follow us for more updates, tips, and opportunities to help you grow personally and professionally. Your future starts here. let’s achieve greatness together! 🚀

Exploring Keras High-level APIs in TensorFlow 2.0:

Kavindu Lakmal Wickramasinghe — Wed, 24 Apr 2024 14:14:18 +0000

In the realm of deep learning, accessibility and simplicity are key factors in driving innovation. TensorFlow, as one of the leading frameworks for machine learning, has continually evolved to make complex tasks more manageable for developers of all skill levels. With the advent of TensorFlow 2.0, Keras became the official high-level API for building and training neural networks. In this blog post, we'll delve into the Keras high-level APIs within TensorFlow 2.0 and showcase their ease of use through illustrative code examples.

Introduction to Keras in TensorFlow 2.0

Keras provides a user-friendly interface for designing and training neural networks, abstracting away many complexities associated with low-level TensorFlow operations. With TensorFlow 2.0, Keras is seamlessly integrated into the framework, allowing for a more cohesive and streamlined workflow.

Building a Simple Neural Network

Let's start by constructing a basic neural network using Keras:

`import tensorflow as tf
from tensorflow.keras import layers

# Define the model architecture
model = tf.keras.Sequential([
    layers.Dense(64, activation='relu', input_shape=(784,)),
    layers.Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
`

In this example, we create a sequential model with two densely connected layers. The first layer has 64 units and uses the ReLU activation function, while the output layer consists of 10 units with a softmax activation function for multiclass classification. We compile the model with the Adam optimizer and specify the loss function and evaluation metric.

Loading and Preprocessing Data

Next, let's load and preprocess a dataset using TensorFlow Datasets:

`import tensorflow_datasets as tfds

# Load dataset
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

# Preprocess data
train_images = train_images.reshape((60000, 28 * 28)).astype('float32') / 255
test_images = test_images.reshape((10000, 28 * 28)).astype('float32') / 255
`

Here, we use the MNIST dataset and normalize the pixel values to the range [0, 1] by dividing by 255.

Training the Model
Now, let's train the model on the MNIST dataset:

`# Train the model
model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.2)
`

We fit the model to the training data for 5 epochs with a batch size of 64, specifying a validation split of 0.2 for monitoring the validation loss during training.

Evaluating the Model

Finally, let's evaluate the model's performance on the test set:

`# Evaluate the model
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)
`

By calling the evaluate method on the test data, we obtain the model's accuracy on unseen examples.

Conclusion

In this blog post, we've explored the Keras high-level APIs in TensorFlow 2.0 through a series of code examples. From building and compiling a neural network to training and evaluating it on a dataset, Keras provides an intuitive interface for deep learning tasks, making it accessible to both beginners and seasoned practitioners alike. With its simplicity and flexibility, Keras empowers developers to focus on the creative aspects of machine learning, driving innovation and advancement in the field.

Thank you for taking the time to read this post, and I hope you found this exploration both enlightening and practical. Happy coding!

Swapping Two Numbers Without a Third Variable: A Simple Trick

Kavindu Lakmal Wickramasinghe — Wed, 17 Apr 2024 05:25:24 +0000

Swapping two numbers is a common task in programming, often used in algorithms and various applications. While the traditional approach involves using a temporary variable to store one of the numbers during the swap, there's an elegant trick in Java to accomplish this without the need for a third variable. In this blog post, we'll explore this efficient technique and implement it in a Java program. By understanding this method, you'll not only save memory but also gain insight into the inner workings of Java's assignment operators. Let's dive in and discover how to perform number swapping without the overhead of an additional variable.

package testing_code;

import java.util.Scanner;

public class SwapNumbers {

    public static void swapNumbersWithoutVariable(int n1,int n2) {
        System.out.println("Before Swapping");
        System.out.println("First number" +n1);
        System.out.println("Second number "+n2);

        n1= n1+n2;
        n2= n1-n2;
        n1= n1-n2;

        System.out.println("After Swapping");
        System.out.println("First number " +n1);
        System.out.println("Second number "+n2);

    }

    public static void main(String[] args) {
        // TODO Auto-generated method stub
        Scanner scanner = new Scanner(System.in);
        System.out.println("Enter a number for number 1 ");
        int n1 = scanner.nextInt();

        System.out.println("Enter a number for numner 2 ");
        int n2 = scanner.nextInt();

        swapNumbersWithoutVariable(n1, n2);

        scanner.close();

    }

}

In this blog post, we explored a clever technique to swap two numbers without using a third variable in Java. Traditionally, swapping involves the use of a temporary variable to store one of the values during the swap operation. However, by leveraging the properties of arithmetic and bitwise operators in Java, we can achieve the same result more efficiently. This approach not only saves memory but also offers insight into the nuances of Java's assignment operators. By understanding this technique, programmers can optimize their code and enhance their problem-solving skills. Join us as we delve into the intricacies of number swapping in Java and uncover the simplicity behind this elegant solution.

Thank you for taking the time to read this post, and I hope you found this exploration both enlightening and practical. Happy coding!
© KAVINDU™ | 2024