Techniques and optimization algorithms in deep learning: A review

Abstract

Deep Learning (DL), a branch of Artificial Intelligence (AI), has transformed various industries by allowing machines to carry out activities that were once thought to be only possible through human intelligence. The rapid progress in deep learning methods and algorithms has played a key role in reaching unparalleled levels of accuracy and efficiency in diverse applications. This research explores the most recent and popular techniques and algorithms in deep learning, offering a detailed look at how they are created and used. Important focuses include convolutional neural networks (CNNs) for recognizing and processing images, recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) for analysing sequential data and language processing, and generative adversarial networks (GANs) for generating authentic synthetic data. Moreover, the research delves into new advancements like transformers and self-attention mechanisms, which have greatly enhanced results in activities such as language translation and text generation. The research also explores new developments in transfer learning, federated learning, and explainable AI, showcasing their ability to improve model generalization, privacy, and interpretability. The study highlights the increased significance of incorporating these advanced methods across different fields such as healthcare, finance, autonomous driving, and robotics, in order to propel the upcoming wave of technological advancements. This research seeks to educate and provide direction to future research and development in the dynamic field of DL by examining advanced algorithms.