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Improve model performance and training stability using multilayer perceptrons (MLPs) and applying normalization techniques.

Implement autoencoders for unsupervised feature learning and design Generative Adversarial Networks (GANs) to generate synthetic images.

Train convolutional neural networks (CNNs) for image classification tasks, understanding how layers extract spatial features from visual data.

Apply advanced architectures like ResNet for deep image recognition and U-Net for image segmentation.

Apply Nonlinear Support Vector Machines (NSVMs) and Fourier transforms to analyze and process visual data.

Use probabilistic reasoning and implement Recurrent Neural Networks (RNNs) to model temporal sequences and contextual dependencies in visual data.

Explain the principles of transformer architectures and how Vision Transformers (ViT) perform image classification and visual understanding tasks.

Implement CLIP for multimodal learning, and utilize diffusion models to generate high-fidelity images.