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