Deep Learning models such as Convolutional Neural Nets (CNNs) and Recurrent Neural Nets (RNNs) are routinely used due to their utility and benefits in various practical applications such as e.g., natural language processing and image recognition. However, despite such adoption, these techniques typically present opaqueness of their internal workings to users. The current black box approach to deep learning makes models difficult to understand and fine-tune, and the related lack of information directly influences productivity and hinders innovations in reliable and consistent model development. In this paper, we present a novel web-based, graphical approach to visualizing CNNs and RNNs to address the above adoption challenges. Our approach features an interactive graphical user interface, where the user can view the overarching network architecture and data flow, the weights and corresponding input processing at each layer, and some interpretable aspects of the model as a whole. We show the effectiveness of our visualization techniques on the MNIST dataset corresponding to an image recognition application. Our work contributes to the effective graphical visualization approaches for complex neural networks and thus makes it easier to manage, manipulate, and increase the performance of these networks.