Robotics and Internet-of-Things Lab

🚀 𝗠𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁: 𝗧𝗵𝗲 𝗠𝗼𝘀𝘁 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗦𝘂𝗿𝘃𝗲𝘆 𝗼𝗻 𝗥𝗢𝗦 𝟮🚀

I am excited to announce that our latest survey paper,

𝗥𝗢𝗦 𝟮 𝗶𝗻 𝗮 𝗡𝘂𝘁𝘀𝗵𝗲𝗹𝗹: 𝗔 𝗦𝘂𝗿𝘃𝗲𝘆

co-authored with Abdulrahman S. Al-Batati and Dr. Mohamed AbdelKader, is now available on Preprints.org! 🎉

Credits go to Abdulrahman S. Al-Batati for the great efforts in gathering this volume of related works and also in building the first repository of ROS/ROS2 publications available at:

📖 𝗥𝗢𝗦/𝗥𝗢𝗦𝟮 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆: https://ros.riotu-lab.org/
📖 𝗙𝘂𝗹𝗹 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gAuigni6

This study stands as the 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝘀𝘂𝗿𝘃𝗲𝘆 to date on the transition from 𝗥𝗢𝗦 𝟭 𝘁𝗼 𝗥𝗢𝗦 𝟮, offering a deep dive into the enhancements, challenges, and future directions for ROS 2.

Our analysis covers:
🔹 Real-time capabilities
🔹 Enhanced modularity
🔹 Security improvements
🔹 Middleware and distributed systems
🔹 Multi-robot system applications

We carefully analyzed 𝟳,𝟰𝟵𝟴 𝗥𝗢𝗦-𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝗮𝗿𝘁𝗶𝗰𝗹𝗲𝘀, with a focused review of 𝟰𝟯𝟭 𝗥𝗢𝗦 𝟮-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗽𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀, making this a key resource for researchers, developers, and enthusiasts in the ROS community.

Our goal is to provide a cohesive synthesis that helps deepen the understanding of ROS 2’s contributions and guides future research in robotic systems design.

Join us in exploring the potential of ROS 2 and shaping the future of robotics! 💡🤖

🚀Understanding the Differences Between LLM Chains and LLM Agent Executors in LangChain🚀

LangChain has become a game-changer for building applications with Large Language Models (LLMs) 🤖. But to fully unlock its power, it's essential to understand how LLM Chains and LLM Agent Executors differ, especially when it comes to using tools 🛠️.

🔗 LLM Chains:

- Organize tasks in a sequence with predefined logic.

- Utilize tools to process information and make decisions in a structured manner 🔍.

🤖 LLM Agent Executors:

- Act as dynamic agents that adapt based on real-time inputs 🌐.

- Use tools more interactively, allowing for flexible problem-solving as they adjust strategies on the go 🔧.

Not sure which one suits your project? 🤔 In my latest blog post, I break down their operational structures, functionalities, and best use cases to help you decide! 💡

➡️ Read more here: https://medium.com//understanding-the-differences-between-llm-chains-and-llm-agent-executors-in-langchain-3f3cf402442f

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LangChain has emerged as a robust framework for building applications powered by large language models (LLMs)🤖. Two fundamental concepts within LangChain are LLM Chains🔗 and LLM Agent Executors🛠️, both of which leverage tools to enhance the capabilities of LLMs.

While they may seem similar at first glance, understanding their differences is crucial for developers aiming to harness LangChain's full potential.

In this blog post, we’ll explore the distinctions between LLM Chains and LLM Agent Executors, their operational structures, functionalities, and ideal use cases.

Understanding the Differences Between LLM Chains and LLM Agent Executors in LangChain LangChain has emerged as a robust framework for building applications powered by large language models (LLMs). Two fundamental concepts…

🚀 New to ? Wondering why and are crucial tools? This lecture is for you!

https://youtu.be/T-qtcmvl8NY

In this comprehensive lecture, we demystify the relationship between Linear Algebra and NumPy, showing you exactly why these concepts are foundational in AI and Data Science.

In this comprehensive lecture, we demystify the relationship between Linear Algebra and NumPy and show you exactly why these concepts are foundational in AI and Data Science.

We seamlessly integrate theory and practice, providing clear explanations and real-world applications.

You’ll learn how key concepts like vectors, matrices, dot products, normalization, and cosine similarity play a vital role in AI and Data Science tasks such as semantic search, word embeddings, and computer vision—all while using NumPy for hands-on implementation.

Whether you’re just starting your data science journey or looking to solidify your understanding, this course offers tangible, practical use cases that will help you see the bigger picture of how Linear Algebra and NumPy power data science applications.

🔗 Watch the lecture now and discover how these essential tools can elevate your data science skills!

مسار تعلم الذكاءالاصطناعي والنماذج اللغوية الكبيرة (LLMs)

إذا كنت طالبًا أو باحثًا يتطلع إلى التخصص في مجال الذكاء الاصطناعي والنماذج اللغوية الكبيرة، فإن بناء مسار تعليمي متين يبدأ بتأسيس قاعدة معرفية قوية في المفاهيم الأساسية.

البداية تكون بفهم عميق لمفاهيم الرياضيات الأساسية - التي عادة لا يحبذها الطلاب - مثل نظريات الإحتملات، الإحصاء، والجبر الخطي، والتفاضل والتكامل من منظور نظري.
هذه المواد تشكل العمود الفقري للعديد من الخوارزميات والنماذج المستخدمة في الذكاء الاصطناعي، مما يجعل إتقانها أمرًا ضروريًا لتحقيق فهم شامل ومتقدم في هذا المجال.

بالتوازي مع الرياضيات الأساسية، يعد تطوير مهارات برمجية قوية أمرًا أساسيا. ينبغي التركيز على تعلم لغتين برمجيتين أساسيتين هما بايثون وC وإدراك مسائل الأداء للبرمجيات من حيث السرعة واستخدام الذاكرة - خاصة في مقررات تصميم الخوارزميات وتراكيب البيانات. هذا يعتبر العمود الفقري لعلوم الحاسب والبرمجة.
تُستخدم بايثون على نطاق واسع في تطوير تطبيقات الذكاء الاصطناعي بفضل مكتباتها المتعددة مثل TensorFlow وPyTorch، بينما توفر لغة C فهماً أعمق لمبادئ البرمجة على المستوى المنخفض، مما يعزز القدرة على تحسين أداء البرمجيات والتعامل مع الموارد بشكل فعال.

بعد إرساء الأساسيات في الرياضيات والبرمجيات، يمكن الانتقال إلى استكشاف تخصصات حاسوبية داعمة مثل أنظمة التشغيل وقواعد البيانات. فهم أساسيات أنظمة التشغيل يساعد في إدارة الموارد وتحسين أداء البرامج، بينما تمكن معرفة قواعد البيانات من التعامل مع تخزين وإدارة البيانات بكفاءة، مما يعد مهماً في معالجة كميات ضخمة من البيانات المستخدمة في الذكاء الاصطناعي.

تأتي المرحلة التالية بتطبيق هذه المفاهيم الأساسية في مجال علم البيانات.
من الضروري إتقان خوارزميات التحسين (optimization) مثل الانحدار التدرجي (gradient descent)، التي تلعب دوراً محورياً في تدريب نماذج التعلم الآلي. هذا التطبيق العملي يربط بين النظرية (الجبر الخطي والإحصاء) والتطبيق (نماذج التنبؤ)، مما يسهل الانتقال إلى مفاهيم أكثر تعقيدًا في التعلم الآلي والذكاء الاصطناعي.

أيضا، من خلال تنفيذ مشاريع عملية وتحليل البيانات، يمكن للمتعلمين تعزيز فهمهم وتطوير مهاراتهم التطبيقية.

مع ترسيخ هذه المبادئ الأساسية وتطبيقها في علم البيانات، يصبح للباحث أو الطالب من الممكن التعمق في مفاهيم الذكاء الاصطناعي والتعلم الآلي المتقدمة بثقة أكبر. التعلم المستمر والممارسة العملية هما مفتاح النجاح في هذا المجال الحيوي والمتطور باستمرار. المشاركة في مشاريع بحثية أو المساهمة في مشاريع مفتوحة المصدر يمكن أن يعزز من المهارات ويوفر خبرات قيمة تساهم في التطور المهني والأكاديمي.

في الختام، بناء قاعدة معرفية قوية في الرياضيات والبرمجة، وتطبيق هذه المفاهيم في علم البيانات، يمهد الطريق نحو فهم أعمق وأشمل لمفاهيم الذكاء الاصطناعي والتعلم الآلي. تذكر أن النجاح في هذا المجال يتطلب التعلم المستمر والمثابرة، بالإضافة إلى القدرة على تطبيق المعرفة النظرية في سياقات عملية متنوعة.

In this video lecture, I provide a comprehensive live demonstration of how to utilize Pandas data structures for data analytics. Using a real-world dataset of Java Programming II grades spanning seven years, I walk through the entire data analysis process.

https://youtu.be/z-C3VdRW4-M

The lecture covers essential data analytics concepts, including:

- Data Cleaning: Techniques for preparing your dataset, including handling missing data and correcting inconsistencies.
- Data Handling: Methods for effectively managing and transforming your data using Pandas.
- Correlation Analysis: Exploring relationships between different variables in the dataset.
- Aggregation: Summarizing data through various aggregation techniques to reveal trends and patterns.
- Descriptive Analytics: Performing statistical analysis to extract meaningful insights from the dataset.

I explain the methodology required for a thorough data analytics study and demonstrate its application with Pandas DataFrames. This lecture is tailored for beginners, providing a clear understanding of how to apply these concepts to real-world data using Pandas.

[CS316] Mastering Data Analytics with Pandas: A Practical Guide using Student Grades Use Case In this video lecture, I provide a comprehensive live demonstration of how to utilize Pandas data structures for data analytics. Using a real-world dataset o...

This video is a new lecture part of the CS316 Introduction to AI and Data Science course.

https://youtu.be/jlAgNG_JvKw

It provides clear and practical insights into the Pandas data structure in Python. It presents Pandas concepts and how to create, access, load, and export data to and from Pandas data frames and series.
It is a good reference for beginners in Python.

[CS316] Pandas Data Structure | Overview, Creation, Access and Load from Data Sources Welcome to our detailed tutorial on Pandas Data Structures, an essential video for anyone looking to master data manipulation and analysis in Python. This vi...

This video is a new lecture part of the CS316 Introduction to AI and Data Science course.
https://youtu.be/jlAgNG_JvKw?si=s2X0Sw8hJ5ITV817

It provides clear and practical insights into the Pandas data structure in Python. It presents Pandas concepts and how to create, access, load, and export data to and from Pandas data frames and series.
It is a good reference for beginners in Python.

🚀 Exciting News! Introducing ExamGPT: Transforming Education with Automated Grading with AI 🚀

🔗 https://youtu.be/qIZ6vMoi084

Over the past year, I've dedicated myself to developing a tool that promises to redefine our approach to educational assessments. Today, I'm thrilled to introduce ExamGPT, a platform where Generative AI and Large Language Models (LLMs) meet educational innovation.

ExamGPT is not just a product; it's a vision realized, a demonstration of how AI can effectively promote and enhance education. This platform ensures timely, personalized feedback, unbiased grading, and a supportive learning environment, transforming both teaching and student experiences across educational landscapes.

From design to a reliable, fully-functional platform, the journey of creating ExamGPT has been driven by the goal to make educational assessments more adaptive and fair. It's designed to assist educators by reducing the grading workload and to empower students with immediate, constructive feedback to foster their learning journey.

Join me in exploring how ExamGPT can make a significant impact in educational settings by providing a scalable solution to one of the most time-consuming tasks—grading.
Here's to a future where educators spend less time grading and more time teaching and where students receive the support they need to succeed!

Transforming Education with ExamGPT: Automating Grading with AI Join us as we explore ExamGPT, a pioneering platform that harnesses the power of Generative AI and Large Language Models (LLMs) to transform the educational ...

I am happy to announce that I have started opening the lectures of the ChatGPT training I did last year.

Video: https://youtu.be/GyFK-NVTOdM

The first open lecture deals with the foundational concepts of building an LLM from scratch. In this lecture, I present the foundational ideas behind modern transformer-based models by tracing the evolution of deep learning models. Starting with the early perceptron, we progress through key advancements in feedforward networks, Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, leading to the development of the transformer architecture.

This lecture breaks down the key building blocks of transformers, explaining their core components and how they process sequential data in a feedforward manner. A must-watch for anyone looking to deepen their understanding of the journey to transformers and how they revolutionized language models.

Build a Language Models using Transformers from Scratch

In this lecture, I cover the essentials of Functional Programming Tools.

Video: https://youtu.be/6UpnKxw3bvg?si=XSJ8UBdHmJ8HtnoW

This session introduces key functional programming tools in Python, such as filter(), map(), and reduce(), highlighting their significance in data science workflows. These functions allow more efficient data manipulation and transformation using concise, expressive code.

Throughout the lecture, I demonstrate practical examples of how these tools can be applied to real-world data science problems, providing viewers with an understanding of how functional programming can simplify complex tasks.

[CS316] Functional Programming Tools | Filter/Map/Reduce In this lecture, Prof. Anis Koubaa covers the essentials of Functional Programming as part of the "Python for Data Science" chapter in the CS316: Introductio...

[CS316] Comprehensive Guide to Python Package Development: From Workspace Setup to Distribution
Video: https://youtu.be/CoaMnsaIHWs
In this video tutorial, I present the complete process of developing a from . It is a part of the CS316 Course(https://ds.riotu-lab.org/).
This video is designed to guide you through every essential step, from setting up a proper and managing with and , to creating and configuring your package, writing a setup.py file, and crafting a professional README.md.
Ideal for both beginners and those looking to refine their skills, this tutorial provides the knowledge and tools needed to confidently develop and distribute your own Python packages.
Key Sections Covered:
- Workspace setup and organization
- Virtual environment management using pip and conda
- Creating and structuring a Python package and module
- Writing and configuring the setup.py file
- Creating a clear and informative README.md file

Watch this video to elevate your Python development skills and master the art of package creation and distribution.

In 4 of the course on Introduction to and , we explored the fundamental crucial in the field.
YouTube Video: https://lnkd.in/dQdc75Sm
Structured Data is highly organized and efficiently managed, typically stored in relational databases. Unstructured Data, on the other hand, lacks a defined format, posing significant challenges for analysis and management. Semi-structured data combines structured and unstructured data elements, exemplified by formats such as and files.
Lastly, Big Data refers to large and complex datasets that require specialized tools and techniques for processing.

Anyone seeking to excel in data science must have a solid grasp of these data types.

To access the course resources, please visit https://ds.riotu-lab.org/.