Perfect answer to this question 😎

(PS: Try at your own risk)

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❕ Uber CEO Dara Khosrowshahi resigns from self-driving truck startup Aurora’s board

Dara Khosrowshahi, CEO of Uber, has resigned from the board of Aurora Innovation, a self-driving truck startup. His decision, effective January 2, 2025, was made to focus on his responsibilities at Uber and to reduce external commitments, with no disagreements reported with Aurora. Khosrowshahi joined Aurora's board after Uber sold its self-driving unit to the company in 2020 as part of a $400 million investment deal.

Shailen Bhatt, COO for AtkinsRéalis, will replace Khosrowshahi on the board. This resignation comes shortly after Aurora's general counsel announced plans to step down. Uber maintains a collaborative relationship with Aurora through its freight platform, although it also partners with other self-driving tech companies.

🔐 Top 10 Features That Make Java Secure 🔐

Java is renowned for its robust security, thanks to these features:

👉 1. JVM (Java Virtual Machine):

Isolates the code execution environment, protecting the host system from malicious code.

👉 2. Security APIs:

Built-in libraries for encryption, authentication, and secure communication (e.g., Java Cryptography Architecture).

👉 3. Security Manager:

Controls application actions at runtime, like file and network access.

👉 4. Void of Pointers:

Eliminates direct access to memory, reducing vulnerability to memory corruption.

👉 5. Memory Management:

Automated garbage collection prevents memory leaks and other misuse.

👉 6. Compile-Time Checking:

Catches errors early, ensuring code integrity before execution.

👉 7. Cryptographic Security:

Advanced encryption and secure data transmission with tools like SSL and digital signatures.

👉 8. Java Sandbox:

Isolates code execution, restricting access to critical system resources.

👉 9. Exception Handling:

Helps prevent unexpected crashes by managing runtime errors effectively.

👉 10. Java Class Loader:

Dynamically loads classes securely, preventing unauthorized code execution.

💡 These features make Java a trusted choice for secure and reliable application development.

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SQL Mindmap

SQL in 30 Days: https://t.me/SQLResourcesTP/6

This is how I would learn PowerBi for 2025:

* Learn to Load Data
* Learn PowerQuery to transform data
* Learn the Star Schema
* Learn DAX to create metrics
* Learn Data Visualisation
* Learn Data Story telling

PowerBi is an intuitive tool when you learn these concepts.
Data Analytics & Visualization Resources: https://t.me/DataAnalysisResourcesTP

Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:

1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.

Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.

Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.

2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.

These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.

Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.

3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.

Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.

4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.

Advancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.

5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.

Join for more: https://t.me/MachineLearningResourcesTP

----------------------------------------------------
AI vs ML vs DS 🤖
-----------------------------------------------------

Artificial Intelligence (AI) 💡
AI stands for Artificial Intelligence, which refers to the idea of imbuing machines with knowledge or intelligence. This concept has been around for about 100 years, focusing on how we can make machines think like humans.

Intelligence and AI 🤔
Intelligence is complex and involves various elements like pattern recognition, creativity, imagination, and emotional intelligence. However, the current pursuit of AI is primarily a subset of true intelligence.

Machine Learning (ML) 📊
Machine Learning (ML) is a method where we don't write explicit rules or code for every condition. Instead, we use data that has both input and output.

Deep Learning 🧠
Deep Learning is a type of machine learning that uses algorithms inspired by the way human brain neurons work.

> Key Differences 🤔
- Machine Learning: Provides data with specific features to classify items.
- Deep Learning: Automatically detects and generates features from raw data.

Why Deep Learning? 📈
Deep learning improves classification and output efficiency by adding multiple layers of neurons. The more data we provide, the better the model's performance becomes.

Machine Learning vs Deep Learning 🤔
- Machine Learning: Works best with smaller datasets.
- Deep Learning: Works best with large datasets.

About 90% of data is considered small data, which is why machine learning is still a viable option. However, deep learning excels with large datasets, making it a powerful tool for complex problems.

Neural Networks and Deep Learning : https://t.me/MachineLearningResourcesTP/3

An important collection of the 15 best machine learning cheat sheets.

1- Supervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf

2- Unsupervised Learning

https://t.me/MachineLearningResourcesTP/6

3- Comprehensive Stanford Master Cheat Sheet

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf

4- Machine Learning Tips and Tricks

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf

5- Probabilities and Statistics

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf

6- Deep Learning

https://t.me/MachineLearningResourcesTP/5

7- Linear Algebra and Calculus

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf

8- Data Science Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf

9- Keras Cheat Sheet

https://t.me/MachineLearningResourcesTP/7

10- Deep Learning with Keras Cheat Sheet

https://github.com/rstudio/cheatsheets/raw/master/keras.pdf

11- Visual Guide to Neural Network Infrastructures

https://t.me/MachineLearningResourcesTP/8

12- Skicit-Learn Python Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf

13- Scikit-learn Cheat Sheet: Choosing the Right Estimator

https://scikit-learn.org/stable/tutorial/machine_learning_map/

14- Tensorflow Cheat Sheet

https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf

15- Machine Learning Test Cheat Sheet

https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/

Neural Networks & Deep Learning
https://t.me/MachineLearningResourcesTP/3

More Machine Learning Resources Here: https://t.me/MachineLearningResourcesTP

WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
ENJOY LEARNING 👍👍

First try = Usually fails
First job = Usually sucks
First post = Usually flops
First message = Usually ignored
First interview = Usually no offer

Don't worry about your first. Just make an attempt, no matter how bad it is. Then you can get better.

To make 2025 your best year yet, focus on eliminating distractions, taking action on your goals, improving continuously, and committing to perseverance. Here are some key strategies to help you achieve success:

Eliminate Distractions

Removing negative influences and minimizing distractions is crucial for maintaining focus on your goals. Consider these tips:

- Turn unnecessary off notifications on your devices
- Create a comfortable and distraction-free work environment
- Practice meditation to improve concentration
- Use visual reminders to stay on task

Take Action on Your Goals

Don't wait for the perfect moment to start working towards your aspirations. Begin today by following these steps:

- Write down your goals and read them daily
- Break larger goals into smaller, manageable tasks
- Schedule time on your calendar for goal-related activities
- Get up early to work on your priorities

Commit to Continuous Improvement

Focus on getting better each day through small, consistent efforts[6]:

- Implement incremental changes in your daily routines
- Conduct regular performance reviews
- Encourage brainstorming and idea-sharing
- Simplify processes to increase efficiency

Persevere Through Challenges

Success requires dedication and resilience. Keep these points in mind:

- Embrace failure as a learning opportunity
- Develop a success-oriented mindset
- Share your goals with others for accountability
- Reward yourself for achieving milestones

Invest in Yourself

Prioritize education and skill development to enhance your long-term success:

- Set aside time for learning new skills
- Attend workshops or online courses
- Read books related to your field or interests
- Seek mentorship opportunities

Key Insights for Success

1. Focus is crucial: Concentrate on what truly matters and eliminate distractions to make significant progress.

2. Action beats perfection: Start working towards your goals immediately rather than waiting for the perfect moment.

3. Embrace growth: Commit to continuous learning and improvement to compound your success over time.

4. Learn from setbacks: View failures as valuable lessons that contribute to your overall journey.

5. Invest wisely: Prioritize education and skill acquisition over material possessions for long-term benefits.

6. Build good habits: Success is a product of dedication and consistent effort.

7. Act with urgency: Remember that time is precious, and the best moment to act is now.

By implementing these strategies and keeping these insights in mind, you'll be well-equipped to make 2025 your most successful and fulfilling year yet. Remember, the key is to start today and maintain your commitment to growth and improvement throughout the year.

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While certificates have their own place to prove your skills, completing a course just for the sake of certificate is not going to help you at all. So whatever courses you take up, please make sure that you learn, practice and acquire that skill.

In every family tree, there is 1 person who breaks out the middle-class chain and works hard to become a millionaire and changes the lives of everyone forever.

May that be you in 2025.

In 1994, people told me programming was for nerds and that I should become a doctor or a lawyer instead.

10 years later, they told me that someone from India would take my job for $5/hour.

Then, no code was going to doom my career.

In 2021, Codex, then Copilot, then ChatGPT, then Devin, then OpenAI o1...

People keep yelling that "Programming is Dead," and yet the demand for good Software Engineers has never been higher.

Stop listening to midwit people. Learn to build good software, and you'll be okay. (Credits: unknown)

https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R

Become a Data Scientist in 2025

Data Science Resources: https://t.me/DataScienceResourcesTP

🔐 Top 10 Features That Make Java Secure 🔐

Java is renowned for its robust security, thanks to these features:

👉 1. JVM (Java Virtual Machine):

Isolates the code execution environment, protecting the host system from malicious code.

👉 2. Security APIs:

Built-in libraries for encryption, authentication, and secure communication (e.g., Java Cryptography Architecture).

👉 3. Security Manager:

Controls application actions at runtime, like file and network access.

👉 4. Void of Pointers:

Eliminates direct access to memory, reducing vulnerability to memory corruption.

👉 5. Memory Management:

Automated garbage collection prevents memory leaks and other misuse.

👉 6. Compile-Time Checking:

Catches errors early, ensuring code integrity before execution.

👉 7. Cryptographic Security:

Advanced encryption and secure data transmission with tools like SSL and digital signatures.

👉 8. Java Sandbox:

Isolates code execution, restricting access to critical system resources.

👉 9. Exception Handling:

Helps prevent unexpected crashes by managing runtime errors effectively.

👉 10. Java Class Loader:

Dynamically loads classes securely, preventing unauthorized code execution.

💡 These features make Java a trusted choice for secure and reliable application development.

More Tips & Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R