Deep Learning: Techniques, Taxonomy, Applications, and Future Directions

Abstract

Deep learning has become one of the key AI techniques of the modern world and has changed many fields by its capability to learn from data, which is often very complex. The presented survey paper aims at describing fundamental techniques and taxonomy of deep learning, various applications, and potential perspectives for further research. The paper starts with presenting the concept of deep learning and its origins as well as emphasizing the important position of DL in the modern development of AI. It then goes further into deep learning workflow and frameworks and presents feedforward neural networks, CNN, RNN, generative adversarial networks, attention mechanisms, and transformers. An overview of deep learning frameworks is discussed, including supervised, unsupervised, reinforcement, transfer, semi-supervised, & self-supervised learning. The survey further continues to provide a detailed description on how DL is being widely applied in CV, NLP, speech recognition, disease diagnosis & stock price forecasting. The issues related to challenges and limitations include data dependency, interpretability, overfitting, high computational burden, and ethical concerns are presented along with ongoing work and directions for future investigations. This includes explainability of AI, incorporation of symbolic AI methods, improving the resilience and security of models, breakthroughs in hardware, as well as expansion into future domains such as quantum computing and robotics. Finally, this survey paper summarizes existing literature and predicts future research direction, emphasizing on how deep learning will revolutionalise technology and how it may foster development in various fields.