A Comprehensive Review of Deep Learning Architectures for Task specific Analysis

Deep learning has truly changed the game 
across numerous fields, reshaping how we 
tackle complex challenges by providing 
highly precise and efficient solutions 
tailored to particular needs. Just picture a 
system that can create text, summarize 
information, translate languages, classify 
data, answer questions, and even reason—
 deep learning makes all of this a reality. In 
this review, we took a closer look at 
different deep learning architectures and 
see how they drive these various 
applications.  We analysed the past studies 
and reveal the datasets that power these 
models, as well as the design principles 
that 
influence 
their 
performance. 
Throughout this we emphasized the 
strengths that set these architectures apart, 
along with the limitations that pose 
challenges to their effectiveness. This 
review acts as a guide for researchers, 
practitioners, and industry professionals, 
helping them choose and adapt the right 
deep learning models for specific tasks.