This project is a structured educational resource and training platform designed for mastering deep learning development. It provides a comprehensive curriculum focused on building, evaluating, and refining predictive models through hands-on coding exercises and standard industry workflows.
The curriculum emphasizes practical implementation, guiding users through the construction of neural network architectures and the application of transfer learning to adapt pretrained models for custom tasks. It includes methodologies for tracking and comparing model experiment results, allowing for the systematic optimization of training configurations and performance metrics.
The resource covers the end-to-end development lifecycle, ranging from initial model design and iterative training to the deployment of predictive models as functional web applications. It specifically focuses on computer vision tasks, providing a guide for implementing image classification and feature extraction models. The content is delivered through a collection of Jupyter Notebooks that facilitate a hands-on approach to learning deep learning fundamentals.
