To Focus on Research Related to Deep Learning and Considering Different Techniques Used in Deep Learning

• Gunjan Arya

  Author

• Dr Narender Kumar

  Author

Deep learning is an area of Artificial Intelligence and Machine Learning that has become a revolutionary way to solve hard problems in many fields. different deep learning methods and how they can be used by looking at secondary data from academic databases, study papers, and other published sources. The paper carefully looks at some important designs, like Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks, pointing out how they are structured differently, how they learn, and how well they work. uses a qualitative and comparative method to find trends, strengths, and weaknesses of each technique by putting together research results from other studies. According to secondary data analysis, convolutional models are great at processing images, recurrent models are good at handling sequential data, and generative models are commonly used for adding to data and making things creative. The study also looks at new developments like transfer learning, reinforcement-based deep learning, and mixed models, which have made models much more accurate and efficient. The results show that deep learning methods can make good predictions and can be used on a large scale. However, problems like data dependencies, high computational costs, and difficulty in understanding the results are still major issues. Why optimized designs are needed and what ethical issues should be thought about when using deep learning systems. This study adds to our overall knowledge of deep learning techniques and lays the groundwork for more real-world studies in the future.

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