Part 1: XOR Dataset Experiments This part involves creating and training models on a generated XOR dataset to explore the capabilities of the quasipolynomial network. Key steps include: Dataset generation. Model training and evaluation. Experimentation with different network configurations.

Part 2: Image Processing Model Here, the quasipolynomial network is applied to an image processing task. This includes: Model design and implementation for image-related operations. Training the model on selected datasets. Evaluation of the model's performance compared to baseline techniques.

Part 3: Replication of Experiments This part focuses on replicating the experiments presented in the main quasipolynomial network paper. The steps include: Reproducing the experimental setup as described in the paper. Validating the results to ensure consistency and correctness. Comparing the replicated results with those published in the paper.

Part 4: QuasiCNN Implementation In this part, a QuasiCNN model is implemented and evaluated. The tasks include: Designing the QuasiCNN architecture. Implementing the forward and backward propagation methods. Testing the model on various datasets to analyze its performance.

Part 5: ResNet Adaptation This part adapts the quasipolynomial network concept to a ResNet architecture. It includes: Modifying the ResNet layers to incorporate quasipolynomial operations. Training and evaluating the adapted ResNet model on standard datasets. Comparing the performance with the vanilla ResNet. (A vanilla ResNet refers to the original ResNet architecture as proposed in the 2015 paper)