Floral Health And Disease Identification Using Machine Learning: A Species Predictor System
Implemented a sophisticated floral health and disease identification classifier, leveraging concepts from the "Flower Categorization using Deep Convolutional Neural Networks" paper, encompassing data loading, normalization, augmentation, and GPU training. Integration of Advanced Deep Learning Models: Utilized and compared CNN architectures such as GoogleNet and AlexNet for accurate identification, demonstrating proficiency in neural network structures and machine learning algorithms.
Artificial Intelligence Visual Recognition & Diagnostic Accuracy Project
Spearheaded the development of a versatile Python-based image classification application, leveraging advanced machine learning libraries. The AI image classifier I developed holds significant implications for various industries. My project contributed to enhancing visual recognition capabilities in autonomous vehicles and improving diagnostic accuracy in medical imaging, showcasing the classifier’s ability to rapidly and accurately process visual data across multiple sectors.
Advanced Canine Breed Identification Using Deep Learning Project
Create advanced deep learning model development for canine breed identification. Engineered and refined a sophisticated image classification system. This project involved the integration of high-level deep learning frameworks and pre-trained models like AlexNet, VGG, and ResNet to accurately distinguish among various dog breeds, demonstrating proficiency in neural network architectures and machine learning algorithm's.