by artificial intelligence and image processing 2. The Modern Mathematics of Deep Learning 3. Compacter: Efficient Low-Rank Hypercomplex Adapter Layers 4. Applications of Deep Neural Networks 5. Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning 6. How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers 7. Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks 8. Nested Variational Inference 9. Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data 10. DeepLab2: A TensorFlow Library for Deep Labeling Pickup!