In this paper we describe our attempt at producing a state-of-the-art senti- ment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our sys- tem leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant su- pervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs to- gether. Our approach achieved first rank on all of the five English subtasks amongst 40 teams.