Effect of Learning Rate on Artificial Neural Network in Machine Learning

Abstract

Machine learning has a wide range of applications in almost every life endeavor. Artificial neural network technique, in particular, has been used to implement prediction and forecasting of results in virtually all works of life including weather, sports, student performance etc. such parameters as momentum, training cycles, and learning rate plays significant roles in the optimization of prediction or forecasting results. This research investigates the effect of learning rate in training a model using the Artificial Neural Network technique. 15 iterative learning rates yielded an undulating graphical representation. The study further shows an 80% prediction with a 0.1 learning rate and a 90% prediction with 0.8 learning rate. This implies that applying the appropriate optimization strategy in machine learning could result in the best possible result.