A hybrid approach for a faster learning of kinetics combining the deep learning of MD trajectory with the WE simulations is presented here. It employs a deep learning architecture to sample statistically relevant conformations representative of the rare events from short MD simulation trajectories. The method is tested on three different model systems, namely, alanine dipeptide with explicit solvation, chignolin with implicit solvation, and the NTL9 protein with implicit solvation. Our method significantly outperforms the WE and the GaMD-WE approach for the chignolin model system, and is comparable in performance for the alanine dipeptide system. Because of its reduced simulation time and slighter sophistication as compared to the GaMD-WE approach, DeepWEST allows for fast estimation of kinetics with enhanced coverage. We have also developed an end-to-end package, DeepWEST, that automates the entire process of running MD trajectories, extracting statistically relevant conformations using VAMP theory and neural networks, and preparing the WE simulation.