Technologies Used
Python
Deep Learning
Neural Networks
Adaptive Learning
TensorFlow
Keras
Project Overview
Developed an adaptive learning algorithm to adjust rates based on rejects, improving neural network reliability by 90% for safety-critical applications. Tested on breast cancer datasets, synthetic data, and MNIST, achieving a 10% accuracy improvement.
Key Features
- Implemented using Python, Deep Learning, Neural Networks
- Developed during May 2023 - Jul 2023
- Focused on performance, usability, and modern design principles
- Utilized best practices for code organization and maintainability
Development Process
This project was developed with a focus on creating a robust and scalable solution. The development process involved careful planning, implementation of key features, rigorous testing, and deployment.
Project Details
Project Type
Python
Timeline
May 2023 - Jul 2023
Primary Technologies
Python
Deep Learning
Neural Networks
Related Skills
PythonDeep LearningNeural NetworksAdaptive LearningTensorFlowKeras