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T cells are a vital part of the immune system, defending us against invading pathogens and cancer. However, T cells can also target non-infected healthy cells of the individual causing autoimmune diseases. The recognition of a target cell, whether disease causing or healthy, is mediated by the T cell receptor (TCR). More specifically the TCR recognizes a peptide fragment, an epitope, presented by the major histocompatibility complex (MHC) by binding to it. Understanding this recognition would be valuable and could be used in many medical applications. In this thesis a deep learning model for the prediction of TCR-peptide-MHC binding is presented. Most current models use the epitopes as a categorical variable, being unable to predict for epitopes outside the training distribution. Our model uses the epitope amino acid sequence and is able to predict for previously unseen epitopes. In addition to the epitope our model uses the MHC allele and the complementarity determining region 3 (CDR3) V and J genes of both chains or either chain of the TCR. The amino acid information of the epitope and TCR are combined using self-attention. We show that different learning rates in the optimization scheme work well for the seen and for the unseen task and how different input features are important for different tasks. The task of unseen epitope prediction is still a very hard task, and the performance is significantly worse than in the seen epitope case. Finally, we show that our model outperforms or is comparable to state of the art methods that are able to predict for unseen epitopes.