Custom loss functions | Example 1: Custom (likelihood/loss) functions | Example 2: Quantile regression | Example 3: Using cito for optimization / active learning

Data Preparation of complex data | The format of the inputs we expect in cito | 1. Preparing your data on disk | 2. Load images into R | 3. Normalize and check channel dimension | 4. Prepare tabular data (response and other tabular data such as altitude and spatial coordinates) | Convolutional neural networks | Multi-modal neural networks | Computational Considerations and Constraints | 1. GPU Requirements | 2. System Memory (RAM) | Example Calculation (500 observations)

Species distribution model - African elephant | Adjusting optimization parameters - Convergence | Train final model with bootstrapping to obtain uncertainties | Predictions | Inference | Multi-species distribution model | Train model with bootstrapping | Advanced: Joint species distribution model

Setup - Installing torch | Details about the internal implementation of ‘cito’ | Internal Data Representation | Construction of the neural networks | Training and Evaluation | Transferability | Introduction to models and model structures | Loss functions / Likelihoods | Data | Fitting a simple model | Baseline loss | Adding a validation set to the training process | Methods | Explainable AI - Understanding your model | Uncertainties/p-Values | Architecture | Activation functions | Tuning hyperparameters | Regularization | Elastic net regularization | Dropout Regularization | Learning rate | Learning rate scheduler | Optimizer | Early Stopping | Automatic hyperparameter tuning (experimental) | Continue training process | The best of both worlds - combining statistical models and deep learning

Possible issues | Convergence issues | Epochs | Learning rate | Solution: learning rate scheduler | Overfitting | Early stopping and regularization