Advancements in Deep Learning: A Systematic Review of its Techniques and Methodologies

Deep learning is currently a hot and exciting topic and is the most efficient, supervised, timeconsuming, and economical method. It adheres to various procedures and topographies which may be applied on complicated problems and large amount of data set. Deep learning (DL) is not a limited learning technique, it is a part of the broader family of machine learning methods based on ANN and representation learning. It is essentially a neural network with more than three or more layers. Deep learning techniques have achieved notable progress in a range of applications, demonstrating noteworthy performance and offering valuable security features. This work primarily reviews the literature on deep learning techniques and applications. The major focus is on the deep learning timeline, including its origins, current state of the art research, assessment criteria, and unresolved challenges. Deep learning's primary contribution is its transformative capacity in several fields, driven by advances in algorithms such as GANs, transformers, and reinforcement learning. These developments have significantly enhanced computer vision for more accurate image and video analysis, generative AI for realistic content creation, and natural language processing for better machine-human interactions. The article concludes with a discussion on the future.