This thesis presents work on recognizing textual entailment (RTE), a relevant subtask for many other natural language processing tasks. Described herein is the approach using deep learning with emphasized benefits of using deep models in contrast to traditionally designed machine learning models using hand-crafted features. Basic concepts of machine learning are explained that are needed for the understanding of presented matter. Three scientific papers are presented which contain novel techniques for textual entailment recognition using deep learning models. The papers are explained, analysed and compared by parts, giving an overview of contemporary approaches to natural language inference using deep learning. The last part of the thesis represents the practical aspect of work, with an implementation of described ESIM model in a different framework (Keras). Existing implementation of said ESIM model was used as a starting point, which differed from the original code. After additional work of aligning that implementation with the original ESIM model code, resulting Keras ESIM achieved results close to the results of the original ESIM model and was used further on. Performance of the model was improved using Bayes optimization of hyperparameters, results of which were presented and analyzed. Beside improving results, Bayes optimization highlighted some hyperparameters as more contributing than others to the model performance, laying ground for further work upon these results. Model was also tested on newly released dataset for natural language inference, MNLI, achieving results comparable to reference ESIM model from MNLI paper. Lastly, knowledge transfer was tested across multiple datasets with results analyzed by category. Compared to the reference model, this thesis' Keras ESIM achieves only marginally lower performance across the board, even surpassing the reference ESIM in some categories. Also, jointly trained ESIM on a combination of MNLI and SNLI datasets shows performance improvements compared to a model trained on a single dataset. Despite MNLI being richer and more language diverse dataset for training deep models compared to SNLI, a significant improvement of performance is demonstrated using a combination of datasets, showing a contribution of SNLI corpus for model training.