I. INTRODUCTION

II. DEEP NEURAL NETWORK (DNN)

III. CONVOLUTIONAL NEURAL NETWORKS (CNN)

IV. ADVANCED TRAINING TECHNIQUES

V. RECURRENT NEURAL NETWORKS (RNN)

VI. AUTO-ENCODER (AE) AND RESTRICTED BOLTZMANN MACHINE (RBM)

VII. GENERATIVE ADVERSARIAL NETWORKS (GAN)

VIII. DEEP REINFORCEMENT LEARNING (DRL)

IX. TRANSFER LEARNING

X. ENERGY EFFICIENT APPROACHES AND HARDWARE FOR DL

XI. HARDWARE FOR DL

XII. FRAMEWORKS AND SDK

XIII. BENCHMARK DATABASES