Gravitational wave astronomy has emerged as a new branch of observational astronomy, since the first detection of gravitational waves in 2015. The current number of O(100) detections is expected to grow by several orders of magnitude over the next two decades. As a result, current computationally expensive detection algorithms will become impractical. A solution to this problem, which has been explored in the last years, is the application of machine learning techniques to accelerate the detection of gravitational wave sources. In this chapter, the application of deep residual networks in achieving rapid detections with high sensitivity is presented. In particular, the AresGW algorithm, implemented using a 54-layer deep residual network, has demonstrated a remarkable ability to achieve a high detection rate in real noise. The results of the first Machine Learning Gravitation Wave Mock Data Challenge (MLGWSC-1) have underscored the effectiveness of the AresGW algorithm, highlighting its higher sensitivity over traditional detection algorithms, such as matched filtering or wavelet-based approaches. The introduction of Deep Adaptive Input Normalization (DAIN) and curriculum learning strategies has allowed substantial improvements in the training efficiency and robustness of AresGW. This progress is poised to bring about a new era in gravitation-wave astronomy, where machine learning will pave new paths for exploration and discovery.