Assessing the Efficiency of Gradient Descent Variants in Training Neural Networks

Neural networking has thus become an important technique in artificial intelligence (AI) because of its ability in relational determination on data. These networks drawing their paradigms from the human brain have greatly transformed such fields as computer vision, natural language processing, and autonomous systems. However, the training of neural networks is still computationally expensive even for today’s commodity hardware, no matter how much simpler networks become more complex and deep. This is due to the fact that it is occasionally necessary to fine-tune millions or even billions of parameters, and this is accomplished through learning procedures.An integral part of this learning is the optimization of a cost function or error function, and in this we most frequently use gradient-based methods.