We are hosting the IS HUB Tech Event!

📍 Location: Digital Library Building, 5th Floor – AAU 4 Kilo
⏰ Time: 7:00 LT

• Opening & Welcome
• IS Hub Introduction
• Guest Sessions & Pitches
• Panel Discussion
• Refreshment Break
• Competitions & Project Demos
• Winner Announcements
• Certificates & Recognitions
• Giveaways & Closing Telegram|LinkedIn|YouTube | Tiktok |

Looking for people who can drop AI, ML, and deep learning resources, tips, and useful stuff in my channel. If that’s you, DM me
@kipa_s

Max 3 ppl

AI / ML / Deep Learning Resources (Part 1)

Here are some high-quality resources to get you started or level up your skills 👇

🎥 YouTube Channels
• 3Blue1Brown → https://www.youtube.com/@3blue1brown
• StatQuest with Josh Starmer → https://www.youtube.com/@statquest

📚 Online Courses
• Kaggle Learn → https://www.kaggle.com/learn
• Applied Data Science Lab → https://www.wqu.edu/adsl
• Learn PyTorch for Deep Learning → https://www.learnpytorch.io/01_pytorch_workflow/
• TensorFlow for Deep Learning → https://dev.mrdbourke.com/tensorflow-deep-learning/00_tensorflow_fundamentals/

🏆 Competitions & Practice
• Kaggle → https://www.kaggle.com/competitions
• Zindi → https://zindi.africa/
• ML Contests → https://mlcontests.com/

💡 Start small, stay consistent, and build projects along the way.

More resources coming soon 👀

@code_it_now

AWS AI & ML Scholars Program 2026 is now open

✅ Amazon Web Services has launched applications for its AI & ML Scholars program, aiming to train 100,000 learners globally in foundational AI and generative AI skills.

✅ The program is designed for students and aspiring technologists with limited access to AI training. No prior experience is required, just curiosity and commitment.

✅ What you’ll get:
📌 Hands-on experience with AWS tools like PartyRock, Amazon Q, and Amazon Bedrock
📌 Project-based learning through AWS Skill Builder
📌 Preparation for the AWS Certified AI Practitioner certification

✅ Program structure:
📌 Challenge Phase (Mar 24 – Jun 24, 2026): foundational training + certificate + 3-month AWS Skill Builder access
📌 Top 4,500 learners advance to a fully funded Udacity Nanodegree
📌 Nanodegree tracks include AI Programmer, Agentic AI Business Professional, and Agent Developer

✨ If you're looking to break into AI or strengthen your practical skills, this is a strong starting point.

🔗 Apply here: https://aws.amazon.com/about-aws/our-impact/scholars/?utm_source=aws_tc_blog&utm_medium=post&utm_campaign=launch_post

@code_it_now

▓▓▓▓▓▓▓▓▓░░░░░░░ ፶፭% (55%)

Is there anyone who sell tiktok coin ?? Dm @kipa_s

▓▓▓▓▓▓▓▓▓▓░░░░░░ ፶፰% (58%)

Join the A2SV Remote Education G7 Program!

The A2SV Remote Education G7 community conversation has officially started! We are excited to invite brilliant members of the A2SV community to join our remote program and take part in this unique learning experience.

🎒Who Can Apply:

Students from all universities are welcome, except the following A2SV partner universities who are not eligible for the remote program (since A2SV provides in-person education at these institutions) :

AAiT, AASTU, ASTU, AUCA and University of Rwanda

Requirements:

Solved ≥60 problems across LeetCode & Codeforces (combined)

≥30 active days across LeetCode and Codeforces combined.

Application Deadline: April 13, 2026

👉Apply here: Application Form

@code_it_now

The importance of chunking extends far beyond simple data organization; it fundamentally shapes how AI systems understand and retrieve information.

Large language models and RAG pipelines require chunking due to their inherent limitations in context windows and computational constraints.

When I process large documents without proper chunking, the system often loses important contextual relationships and struggles to identify relevant information during retrieval. Effective chunking directly enhances retrieval precision by creating semantically coherent segments that align with query patterns and user intent.

In my experience, well-implemented chunking strategies significantly improve semantic search capabilities by maintaining the logical flow of information while ensuring each chunk contains sufficient context for meaningful embeddings. This approach allows embedding models to capture nuanced relationships and enables more accurate similarity matching during retrieval.

▓▓▓▓▓▓▓▓▓▓░░░░░░ ፶፱% (59%)

Ministry of peace

Leeds united

Retrieval Augmented Generation (RAG) is one of the most popular techniques to improve the accuracy and reliability of Large Language Models (LLMs). This is possible by providing additional information from external data sources. This way, the answers can be tailored to specific contexts and updated without fine-tuning and retraining.

Despite the popularity of RAG, it doesn’t work well in all contexts. In this article, we’ll explain step-by-step how to build RAG pipelines effectively, explore the limitations of RAG, and how to address these limitations.

seeing the power of math in this video, I remembered smth. when we were in high school almost we all asked "why do we learn math?" did u any of u say 'we learn it to boost our thinking skill and problem solving"? or did u say, "it is just finding the value of x and y for years that nobody has got"?😂 Yh and there’s actually a very practical answer now. A lot of us in high school asked, “Why are we learning math? Is it only solving for x and y forever?” 😂But math quietly ended up powering a lot of the technology we use tdy especially search, information retrieval, and modern AI. ena When you search for a document, the computer usually doesn’t “understand” words the way humans do. It first converts text into numbers. That process is called an embedding. An embedding turns a word, sentence, or whole document into a vector basically a list of numbers that captures meaning. lemsale, words like “university,” “student,” and “campus” often end up closer together in that mathematical space than unrelated words like “banana” or “engine.”(sill they hv similarity but it is much too low.)
Then math helps compare those vectors. One common method is cosine similarity. equatinu degmo paper lay ale
Instead of checking whether two texts use exactly the same words, cosine similarity checks whether their vectors point in similar directions. If they do, the meanings are probably related. A simplified version of the process looks like this: mejemereya You ask a question kezia The system converts your question into an embedding (numbers) after that It already has stored embeddings for documents. then It compares your question vector with document vectors. finally. The documents with the highest similarity are retrieved.
That is a big part of vector space retrieval in the field of Information Retrieval.This is also how many RAG systems work. Before the AI writes an answer, it first retrieves the most relevant chunks of information using mathematical similarity. Then the model uses those retrieved pieces as context to generate a better answer.
So the funny thing is bzu sew a lot of what looked like “just x and y” in school later becomes vectors, distances, similarity scores, optimization, and probability and that’s part of what makes search engines and modern AI work.
and honestly AAU Chat bot ena Adwa AI assistant bezi new yeseranew.

@code_it_now

▓▓▓▓▓▓▓▓▓▓▓░░░░░ ፷፭% (65%)

BoA and dashen super app has more bug than በግ ተራ