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 when the images are held in memory (500 observations)

Installing the R package | Working with sjSDM | Fit a basic jSDM | Interpreting the estimated model coefficients | Interpreting the environmental component | Interpreting the spatial component | Interpreting the species-species covariance (associations) component | ANOVA and variation partitioning | Global ANOVA | Calculating the internal metacommunity structure | Testing for predictors of the assembly processes | Predictions | Advanced topics | Fitting other distributions (e.g. species frequencies) | Poisson | Negative Binomial | Normal (gaussian) | Modifying the spatial component | Using Moran's eigenvector map predictors | Trend surface model - linear | Trend surface model - DNN | Adjusting regularization parameters | Regularization on abiotic coefficients | Regularization on species-species associations | Regularization on the spatial model: | Using deep neural networks | Installation problems | Check what is missing | Behind a proxy or without an internet connection | GPU support | Migrating from sjSDM 1.0.x | Help and bugs

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

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