Seeking an entry-level Machine learning engineer/Intern role to apply academic programming skills in active projects, aiming to learn from experienced professionals and contribute effectively to innovative solutions.
Developed and implemented deep learning models using TensorFlow and Keras to recognize sign language gestures.
Pre-processed and augmented a large dataset of sign language images to improve model generalization and performance.
Trained the models using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to interpret gestures with 15% precision.
Optimized model hyper parameters, including learning rate and batch size, to enhance accuracy and reduce misinterpretation errors.
Conducted extensive testing and validation, including cross-validation and 20% data stratification, to ensure the robustness and reliability of the models.
Collaborated with sign language experts to refine model outputs, incorporating feedback to improve recognition accuracy and user experience.
Designed and developed an innovative system architecture for automatic garbage management.
Integrated sensors for 20% actual garbage level detection in bins.
Implemented an automated mechanism for efficient waste collection and disposal, reducing manual intervention and improving operational efficiency.
Integrated IoT technologies to enable remote monitoring and control of the garbage management system, enhancing its scalability and usability.
Collaborated with stakeholders including local authorities and 15% equipment developers to ensure the system's effectiveness and sustainability.
Led testing and validation to ensure the system's reliability and performance under various environmental conditions.
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