Title: Gradient Boosting Neural Networks: GrowNet. Abstract: A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''. General loss functions are considered under this unified framework with specific examples presented for classification, regression and learning to rank.  A fully corrective step is incorporated to remedy the pitfall of the greedy function approximation of the classic gradient boosting decision tree. The proposed model rendered outperforming results against state-of-the-art boosting methods in all three tasks on multiple datasets. An ablation study is performed to shed light on the effect of each model component and model hyperparameters.