Abstract
Improving Learning Performance in Neural Networks
Highlights
A large number of optimization research studies has been contacted frequently using efficient deep learning models, e.g., reinforcement learning [1], self-supervised learning [2], unsupervised learning [3], pruning of decision trees [4], swarm intelligence [5], etc
The networks technically adjusted using a back-propagation algorithm to observe errors in each node based on the difference between planned output and observed output
Improving the solutions constructed by nodes is the main challenge in neural network algorithms
Summary
A large number of optimization research studies has been contacted frequently using efficient deep learning models, e.g., reinforcement learning [1], self-supervised learning [2], unsupervised learning [3], pruning of decision trees [4], swarm intelligence [5], etc. In our perspective, learning in a neural network could be optimized by increasing the pheromone level deposited for the transition from node to node in its space This technique could influence and control parameters toward the optimal solution. Unlike previous methods proposed by more recent researchers [6][7], the goal is not to deal with the process of learning, but rather it is important to deal with the outcomes of learning to better increase the performance of optimization. Association has been developed a technique for learning optimization algorithms for highdimensional random optimization issues, and just like the downside of practicing shallow neural networks. Changing the attributes of neural networks like weights and learning rates to reduce the losses and provide the most accurate results needs methods or algorithms.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Hybrid Information Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
