dan.
Artificial
Intelligence/Backend
Engineer
amazing cases
Survprompt
Survprompt is an advanced surveillance system designed to enhance camera footage analysis and retrieval using state-of-the-art technologies. It provides a range of key features including footage and image retrieval using prompts, notifications and alerts for specific events, event predictions, and advanced analysis based on camera captures. Key features include:
- Footage/Image Retrieval: Retrieve specific footage or images using text prompts.
- Notifications and Alerts: Receive notifications for specific events, configured via prompts.
- Event Predictions: Provides predictive insights based on captured events.
- Advanced Analysis: Analyzes camera footage to recognize objects, track movements, and summarize scenes.
Technologies Used:
- Kafka: For messaging and streaming data.
- Chroma DB: Vector database for storing embedded vision records.
- LLM (Large Language Model): Powers the conversational interface.
- NFS (Network File System): For file storage and sharing.
- Embeddings: Converts vision records into searchable vectors.
- Kubernetes: For container orchestration and management.
- Flask: Web framework for the API.
- OpenCV: Image processing library.
- YOLO (You Only Look Once): Object detection algorithm.
- RunPod Serverless: Serverless GPU infrastructure for scalable processing.
Survprompt combines cutting-edge technologies to offer a robust solution for managing and analyzing surveillance footage, simplifying the process of retrieving, analyzing, and acting on camera data.
personal projects
Realtime Face Recognition
The system is capable of processing live video feeds to accurately recognize and label individuals in real-time, ensuring high accuracy and speed.
This project demonstrates expertise in computer vision, deep learning, and real-time data processing.
AI Hand Gesture Recognition
Successfully completed a Hand Sign Recognition System capable of identifying gestures such as Cool, Peace, Hi, and One.
It demonstrates proficiency in gesture recognition algorithms, feature extraction, and pattern matching, ensuring high accuracy and responsiveness.
AI Body Decoder System
Successfully built an Artificial Intelligence Body Decoder System to identify emotional and physical states such as Sad, Happy, Neutral, Wakanda, and Victorious.
This system utilizes advanced machine learning and computer vision techniques to analyze body language and facial expressions in real-time.
AI-based Sentiment Analyser deployed using Flask
Completed and deployed an AI-based Sentiment Analyser on Heroku. The application takes a user’s review as input and then measures the attitude/sentiment of that user towards the aspect of a movie which can either be Positive or Negative.
This project demonstrates expertise in computer vision, deep learning, and real-time data processing.
show more projects
about me
Daniel Adama
I am a Machine Learning Engineer with 3+ years of experience in Data Analytics and Computer Vision, proficient in Python and SQL. I excel in data preparation, model building and deployment, and evaluating algorithms to improve performance for regression, classification, gradient boosting, KNN, object detection, NLP, and recommender systems. Skilled in optimizing CNNs and employing deep learning methodologies, I am eager to apply my expertise to a challenging role in the machine and deep learning space.
Tools and Languages
Python, SQLite, MySQL, Docker, Git, Linux, Jupyter Notebook, Heroku, Tensorflow, Deep Learning, Neural Networks (CNN). and others...
Technical Skills
Regression(Linear/Logistic, Lasso and Ridge), Gradient Boosting, KNN, Data Manipulation, and Analysis, Python (Scikit-learn, Numpy, OpenCV, Mediapipe, Pytesseract, Matplotlib, Pyaudio, Seaborn, Deep Speech, Face recognition, Dlib.)
Work Experience
- Software Engineer
- Machine Learning Developer
- IoT Computer Vision Intern
- Machine Learning Intern
- Telecommunication Engineering Intern
Education
- Bachelor of Engineering (B.E.) Information and Communication Engineering
- Covenant University
Certifications
- Getting started with Docker (Simplilearn)
- Machine Learning Certification Course (Analytics Vidhya)
- Neural Networks and Deep Learning (Deeplearning.ai)
- Data Science Course 2020 (365 Data Science)
- OpenCV Basics - OpenCV101 (Pyimagesearch)
- Bounding Box Regression - Object Detection 202 (Pyimagesearch)
- Fundamentals of Deep Learning Object Detection - Object Detection 201 (Pyimagesearch)
- Figma, Adobe XD, Sketch
contact me
download my cv
dan.
design & coding by me