Abstract: Today, cryptocurrency investors need accurate predictions to make optimal investment decisions that maximize profits or minimize losses. Moreover, accurate predictions can help policymakers and regulators understand the behaviour of cryptocurrency markets. Cryptocurrency prices, however, are difficult to predict due to their fragility and complexity. Recently, Deep Learning (DL) has been used and presented in academic literature as a potential successor to traditional statistical and machine learning methods for time series problems. DL techniques can efficiently handle time series problems such as nonlinear relationships, multivariate inputs due to their powerful ability to learn from raw data. In this empirical study, the top two current cryptocurrencies, Bitcoin and Ether, are used to compare the performance of 6 DL techniques. These are Fully Connected Neural Network (FCNN), Long-Short Term Memory (LSTM), Bi-directional Long-Short Term Memory (BLSTM), Convolutional Neural Network (CNN), and two other different hybrid architectures constructed using these techniques. The results show that DL techniques effectively predict the prices of cryptocurrencies, with an overall Average Mean Absolute Percentage Error (MAPE) of 9.76, Root Mean Squared Error (RMSE) of 5007.75, and prediction accuracy of 90.24% for Bitcoin. For Ether, the overall averages were a MAPE of 16.41, RMSE of 765.87, and prediction accuracy of 84.13%. The combination of CNN, LSTM followed by FCNN in a hybrid architecture led to the best prediction results for Bitcoin. This combined model outperformed both the basic model and all other models. It achieved a 2.83% lower MAPE, and a 3.03% lower RMSE. On the other hand, CNN's performance on Ether was marginally better than the base model, with a 0.47% lower MAPE and a 0.54% lower RMSE.