Welcome to my Portfolio
I'm an AI and Full-Stack Engineer focused on building intelligent systems using machine learning and deep learning. I specialize in developing end-to-end AI solutions, from data preprocessing to model optimization and deployment, and I'm passionate about solving real-world problems with data-driven approaches.
Contact & Profiles
AI Projects
fine-tuning-qwen2-5-vl-astronomy
A lightweight end-to-end pipeline that fine-tunes Qwen2.5-VL-7B using LoRA adapters on an astronomy image-caption dataset, then deploys the adapted model via a Gradio interface, all with minimal GPU memory using 4-bit quantization.
Unsloth_Llama_3.2_11B_Vision_Instruct_Astronomy
Fine-tuning Llama 3.2 11B Vision on an astronomy image-caption dataset using Unsloth and LoRA, achieving domain-grounded visual descriptions in under 12 minutes of training on a free-tier Tesla T4 GPU.
ResNet50V2-COVID-19-Classification-on-Chest-X-ray-Images
A deep learning pipeline for detecting COVID-19 from chest X-rays using ResNet50V2 with Grad-CAM. The project covers data exploration, preprocessing, augmentation, model training, evaluation, and interpretability, demonstrating accurate, explainable classification across multiple radiography classes.
PyTorch_CNN_vs_Transformer_vs_Xception
Compare three PyTorch image classification approaches - Custom CNN, DeiT-Tiny, and Xception with transfer learning - on vehicle data. Transfer learning improves accuracy, generalization, and reliability on small datasets, while training from scratch struggles with variance and real-world performance.
Sports_Ball_Classification_Inceptionv3
Deep learning sports ball classifier using InceptionV3 transfer learning. Features comprehensive data preprocessing, two-stage training, and FastAPI deployment. Includes data balancing, quality analysis, and rigorous evaluation metrics.
High-Accuracy-Cats-vs-Dogs-InceptionV3
In this notebook, I build a high-accuracy Cat vs. Dog classifier using InceptionV3 and transfer learning. After thorough EDA and robust tf.data preprocessing, the model achieves ~99% test accuracy with strong generalization and no overfitting.
Brain-Tumor-Classification-with-Grad-CAM
A deep learning pipeline for classifying brain tumor MRI images using InceptionV3 and Grad-CAM. Includes full preprocessing, augmentation, tf.data pipelines, transfer learning, evaluation metrics, and interpretable heatmaps for reliable medical image analysis.
Audio-Classification-Raw-Audio-to-Mel-Spectrogram-CNNs
Complete end-to-end audio classification pipeline using deep learning. From raw recordings to Mel spectrogram CNNs, includes preprocessing, augmentation, dataset validation, model training, and evaluation, a reproducible blueprint for speech, environmental, or general sound classification tasks.
Animal-Image-Classification-Using-InceptionV3
This project builds an animal image classification system using TensorFlow and InceptionV3. It includes a full data-cleaning pipeline that detects corrupted images, duplicates, brightness issues, and mislabeled samples before training a transfer-learning model for accurate and reliable predictions.
CNN_Autoencoder_For_Image_Denoising
A U-Net based CNN autoencoder designed to denoise noisy images before classification, improving input quality and boosting overall model accuracy.
Full-Stack Projects
RehanPulse
RehanPulse – A real-time developer dashboard that unifies GitHub activity, Vercel deployments, and Firebase project data into a single live-updating interface with alerts and a customizable experience, powered by an integrated LLM named “Pulse AI” to assist users with insights, automation, and intelligent support.
SpendMetra
SpendMentra is a modern web app for managing personal finances. It lets users track income and expenses, categorize transactions, set financial goals, and view detailed reports. Built with React, Tailwind CSS, and Firebase, it offers secure authentication, real-time data storage, and a responsive design.
Experience
Smart Classroom – Graduation Project
Arab Academy for Science, Technology & Maritime Transport (AASTMT)
Alexandria, Egypt
Oct 2023 - Feb 2024
Designed and developed an intelligent smart classroom system integrating AI, IoT, and embedded systems for real-time environment monitoring and automated device control.
- •Implemented YOLO-based object detection for classroom monitoring
- •Deployed solution on Raspberry Pi using edge computing
- •Automated lights, AC, projectors, and curtains using motion and temperature sensors
- •Developed a Flutter mobile application for remote control and monitoring
- •Integrated AI models with IoT hardware for real-time intelligent decision-making
AI Engineer (Personal Projects)
Self-Employed / Independent Projects
Ajman, UAE
Jan 2022 - Present
Designed and implemented multiple AI and Machine Learning projects to strengthen practical understanding of ML, Deep Learning, and data-driven problem solving.
- •Built and trained ML models from scratch
- •Implemented Neural Networks including Perceptron and XOR Machine Learning projects
- •Developed data preprocessing and feature engineering pipelines
- •Applied model evaluation techniques (ROC, AUC, R², Cross-Validation)
- •Integrated projects with databases (Supabase)
Certifications
Bachelor of Science in Computer Science
Arab Academy for Science, Technology & Maritime Transport (AASTMT)
Studied core Computer Science subjects including programming, data structures, algorithms, databases, artificial intelligence, and software engineering. Gained strong analytical and problem-solving skills through academic coursework and practical projects.
February 20, 2024
AI Trainer
Digital Hub
Completed digital transformation training program Track: Artificial Intelligence, Associate Level based IBM.
January 1, 2023
IoT Trainer
Digital Hub
Completed digital transformation training program Track: IoT, Associate Level based IBM.
January 1, 2023
Cisco Networking Academy Certificates
CISCO
Cisco Networking Academy Certificates – Networking Fundamentals, Routing & Switching, IT Essentials.
January 1, 2022
View Certificate →Soft Skills
HRDC (Cairo)
Certified in Soft Skills, including communication, teamwork, and problem-solving.
August 31, 2021
Hult Prize Regional Finals Competitor
Cairo Impact Summit
As a Competitor in the Regional Finals, hosted at the 2021 Cairo Impact Summit.
April 1, 2021
Published Articles
Building RehanPulse: A Real-Time Developer Dashboard with Next.js, Firebase, and Server-Sent Events
RehanPulse is a real-time developer dashboard that centralizes activity from tools like GitHub, Vercel, and Firebase into a single interface. It delivers live updates on commits, CI pipelines, deployments, and alerts using Server-Sent Events and Firestore listeners, removing the need to switch between multiple tabs, and is built with modern technologies like Next.js and TypeScript.
Building a Secure Personal Finance Tracker: Lessons from Production
Discussing building FinanceTracker, a secure personal finance app. Highlighting key choices in technology, authentication, and data security, showing how careful design ensures usability, privacy, and reliability for managing personal finances.
Fine-Tuning Qwen2.5-VL for Astronomy with Unsloth: A Compact End-to-End Workflow
A compact, end-to-end guide to fine-tuning Qwen2.5-VL, a vision-language model, on astronomy-specific data using Unsloth, covering the full workflow from dataset preparation to training, with a focus on efficiency and minimal resource usage.
Fine-Tuning Llama 3.2 11B Vision on an Astronomy Dataset with Unsloth
Fine-tuning Meta's Llama 3.2 11B Vision model on a 250-image astronomy dataset using Unsloth and LoRA, completing training in under 12 minutes on a free Tesla T4 GPU, producing a model that accurately describes astronomical images and is deployed via Gradio on Hugging Face.
Detecting COVID-19 from Chest X-rays: A Deep Learning Approach using ResNet50V2 with Grad CAM
A deep learning pipeline using transfer learning to classify chest X-rays into COVID-19, normal, viral pneumonia, and lung opacity. Includes data exploration, cleaning, augmentation, model training, evaluation with metrics like confusion matrices and ROC curves, and interpretability via Grad-CAM visualizations.
Comparing Three Computer Vision Approaches in PyTorch: From Custom CNNs to Advanced Transfer Learning
Comparing custom CNNs, Hugging Face vision transformers, and Xception transfer learning for vehicle classification, showing that transfer learning - especially Xception with two-phase fine-tuning - greatly outperforms training from scratch on small datasets.
Classifying Sports Balls with Deep Learning: A Practical Journey Through Transfer Learning
A practical deep learning journey using transfer learning with InceptionV3 to classify sports balls. This project covers data cleaning, class balancing, preprocessing pipelines, two-stage training, and rigorous evaluation - highlighting why data quality and smart engineering matter more than model complexity.
Building a Near-Perfect Cat vs. Dog Classifier with InceptionV3
High-accuracy InceptionV3 pipeline for cat vs. dog image classification with robust preprocessing.
Brain Tumor Classification Using InceptionV3 and Grad-CAM: A Deep Learning Pipeline
End-to-end deep learning pipeline using InceptionV3 and Grad-CAM for accurate and interpretable brain tumor MRI classification.
Building a Complete Audio Classification Pipeline Using Deep Learning: From Raw Audio to Mel Spectrogram CNNs
Built an end-to-end audio classification pipeline using CNNs on Mel spectrograms, with data cleaning, augmentation, and deep learning for reliable multi-class predictions.
Building a Clean, Reliable, and Accurate Animal Classifier Using InceptionV3
Built an animal image classification pipeline using TensorFlow and InceptionV3, focusing on thorough data cleaning, preprocessing, and transfer learning for accurate results.
A U-Net–Based CNN Autoencoder for Cleaning Noisy Images Before Classification
A practical walkthrough of how I built and trained a deep-learning model to denoise images and boost classification performance.
Interested in collaborating?
I'm always open to new opportunities and interesting projects.
Feel free to reach out!
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