Advancements in deep learning: from theory to application using natural language processing
Madhav Sharma, Payal Bansal, Ritam Dutta, Hukam Chand Saini, Shalini Chaudhary, Debmitra Das · IET conference proceedings. · 2025
Deep analyzing has witnessed excellent enhancements in modern-day-day years, mainly in its software program to natural language processing (NLP). This paper offers an entire assessment of the evolution of deep learning strategies, tracing their journey from theoretical foundations to sensible packages in NLP. The theoretical underpinnings of deep getting to know, which incorporates neural network architectures together with convolution neural networks and recurrent neural networks are mentioned, highlighting their capability to capture complicated styles in data. We delve into the stressful conditions faced via manner of conventional NLP techniques and the way deep reading has addressed the ones demanding conditions with the useful resource of allowing greater nuanced records of language via techniques which encompass phrase embeddings and interest mechanisms. Moreover, the paper explores contemporary upgrades in deep studying models tailored specially for NLP obligations, together with transformers and pre-informed language fashions like BERT, GPT, and XLNet. These models have revolutionized the arena through achieving extraordinarily-modern-day regular overall performance throughout severa NLP benchmarks, starting from sentiment assessment and named entity recognition to gadget translation and question answering.