icnnprocess

Data Augmentation Using CTGAN:

First, we used CTGAN for data augmentation, as detailed in the previous merge request. This helps generate synthetic data for better model training. Data Preprocessing:

After augmenting the data with CTGAN, we applied data preprocessing techniques like centering and other required transformations to prepare the data for the ICNN model. Model Architecture:

The ICNN model is then trained on this preprocessed and augmented dataset. It uses convolutional layers to capture important features in the dataset, improving the classification results for the IDSC 2017 dataset. Training on IDSC 2017:

Once the ICNN model is trained, it will be used to classify the IDSC 2017 dataset. The goal is to accurately classify different categories and improve the model’s performance by using high-quality synthetic data created by CTGAN. Integration with SageMaker:

The model training process is integrated into SageMaker using SageMaker pipeline. This will automate the training process and allow hyperparameter optimization to fine-tune the model for optimal performance. Hyperparameter Tuning and Deployment:

Once the model is trained, we will use SageMaker hyperparameter tuning to optimize the model configuration. After tuning, the model will be deployed using SageMaker's deployment services.