In recent years, we have seen many spectacular successes achieved by means of deep learning techniques. Deep neural networks were successfully applied to tasks in which traditional machine learning algorithms could not succeed – large-scale image classification, autonomous driving, and superhuman performance when playing traditional games such as Go or classic video games. Almost yearly, we can observe the introduction of a new type of network that achieves state-of-the-art (SOTA) results and breaks some kind of performance record.
With the constant improvement in commercially available Graphics Processing Units (GPU), the emergence of freely available processing power involving CPUs/GPUs (Google Colab, Kaggle, and so on) and the rapid development of different frameworks, deep learning continues to gain more and more attention among researchers...
