Epilepsy is a complex disease, difficult to detect and common among neurological diseases. The separation of epileptic and non-epileptic activity and the identification of the form of the epileptic activity on the electroencephalogram (EEG) plays an essential role in providing correct treatment to patients. At the same time, detection of epileptic seizures is very important for patients. In this study, a method to classify EEG data using deep neural network architecture is presented. In the presented method, bidirectional long-term memory (Bi-LSTM), a type of recurrent neural network (RNN) was used. Single-layer Bi-LSTM architecture memory units, neural numbers and learning algorithms were modeled. After determining the architectural hyperparameters, the single layer 10-neurons Bi-LSTM approach is proposed as the optimum model. In this model, the effect of both optimization algorithms on classification success and changing the number of neurons on performance was investigated. In the classification between epileptic activity and other activities, the success rate was 96%, success rate in binary classification 99% and the mean success rate for classification was 97.78%. Unlike previous work, this study did not just focus on success rates. In addition, the importance of determining the hyperparameters (selection of optimization algorithms, initial learning rate, number of neurons) in the Bi-LSTM model was highlighted and the effects of these choices on accuracy rates was determined. Success rate and processing load were examined in detail. This study, which emphasizes the importance of parameter optimization, provides an important perspective to this research area.