The Latest Trends in Deep Learning Optimization Methods

In 2011, AlexNet’s achievement on a prominent image classification benchmark brought deep learning into the limelight. It has since produced outstanding success in a variety of fields. Deep learning, in particular, has had a significant impact on computer vision, speech recognition, and natural language processing (NLP), effectively reviving artificial intelligence. Due to the availability of extensive datasets and good computational resources, Deep Learning has even prospered to a whole new level. Although massive datasets and good computational resources are there, things can still go wrong if we cannot optimize the deep learning models properly. And, most of the time, optimization seems to be the main problem for lousy performance in a deep learning model. The various factors that come under deep learning optimizations are normalization, regularization, activation functions, weights initialization, and much more. Let’s discuss some of these optimization techniques.