Natnael Dejene

Detail-oriented Computer Science graduate with robust expertise in Python and a strong focus on Artificial Intelligence, Machine Learning, and Deep Learning. Currently pursuing a Master of Science in Electrical and Computer Engineering, specializing in neural networks and AI solutions. Proven experience in developing AI models for image classification and crop disease detection, with aspirations to innovate in document processing automation and explore Generative AI applications. Combines analytical prowess with critical thinking to excel in data-driven environments.

• CodePath, Student, Web Development
• Tech Excellence Training

Endangered Animal Classification, Developer (Team of 3), https://github.com/nateej/deep-learning-dataset, Developed an AI-based image classification model using Python and TensorFlow., Employed transfer learning with a customized ResNet50 model to achieve 85% accuracy across 10 endangered animal classes., Collaborated with project teams, architects, and contractors during site visits, submittal reviews, and contract administration duties., Tailored the model by replacing the final fully connected layer to address specific classification tasks. Crop Disease Detection, Developer, https://colab.research.google.com/drive/1mILcomEwM6ziN7CCF15NGYLE5asGcDaL?usp=sharing, Engineered a deep learning model leveraging the VGG19 architecture to classify plant diseases across 38 categories from a dataset of 87,867 images., Applied transfer learning, fine-tuning, and data augmentation techniques, achieving 99.4% test accuracy., Integrated advanced methods including class weights, dropout layers, and categorical crossentropy loss on GPU-accelerated hardware.

Python, Java, JavaScript, HTML, CSS, TensorFlow, Deep Learning, Neural Networks, Pandas, NumPy, React, Express, Mongoose, CodePath Web Development