Alzheimer's disease (AD) is one of the deadliest neurodegenerative diseases ailing the elderly population all over the world. An ensemble of Deep learning (DL) models can learn highly complicated patterns from MRI scans for the detection of AD by utilizing diverse solutions. In this work, we propose a computationally efficient, DL-architecture agnostic, ensemble of deep neural networks, named 'Deep Transfer Ensemble (DTE)' trained using transfer learning for the classification of AD. DTE leverages the complementary feature views and diversity introduced by many different locally optimum solutions reached by individual networks through the randomization of hyper-parameters. DTE achieves an accuracy of 99.05% and 85.27% on two independent splits of the large dataset for cognitively normal (NC) vs AD classification task. For the task of mild cognitive impairment (MCI) vs AD classification, DTE achieves 98.71% and 83.11% respectively on the two independent splits. It also performs reasonable on a small dataset consisting of only 50 samples per class. It achieved a maximum accuracy of 85% for NC vs AD on the small dataset. It also outperformed snapshot ensembles along with several other existing deep models from similar kind of previous works by other researchers.