부산대학교 도서관

Deep Learning comes under Machine Learning that accomplishes more power and flexibility by learning to present different concepts or relations of real world to simpler concepts. We use Deep learning fundaments in this paper because it has massive amount of data that helps in innovations. We include these neural networks of deep learning because it comes with a high accuracy rate with lower computations. Natural Processing Language (NLP) and Generative Adversarial Network (GAN) are the methods that individually contribute to the text generation method. Although these are two different technologies giving the output for some common motive where Text generation plays a very important role in smart translations and dialogue systems. This review paper presents a model centered around text generation. This is done because combinedly we want to present what can be different approaches to look at a model like this. To solve the problem of unnecessarily used large texts, unsatisfactory feedback, NLP is used for text generation, GANN is used for text generation model, image generation etc. Finally, this is done to reduce time complexities, speed, efficiency in process because this is noticed that learning for a problem plays a vital role in education to enhance features.