T12: Implementing Mixed Precision and Quantization Aware Training

Welcome to the experiment focused on implementing mixed precision and quantization aware training (QAT) to optimize deep learning models. In this experiment, you will explore techniques like mixed precision training using 16-bit and 32-bit floating-point arithmetic, as well as QAT, which helps in reducing model size while maintaining accuracy. These techniques not only make training faster and less resource-intensive but also improve model performance when deployed on resource-constrained devices.