Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of updating the weight parameters after assessing the entire dataset, Mini Batch ...

Abstract: As the size of base station antenna arrays continues to grow, even with linear processing algorithms, the computational complexity and power consumption required for massive MIMO ...

Laboratory of Matter, Environmental and Solar Energy Sciences (LASMES), UFR Sciences of the Structures of the Material and Technology, Félix Houphouët Boigny University, Abidjan, Côte d’Ivoire. In ...

In this tutorial, we demonstrate how to efficiently fine-tune the Llama-2 7B Chat model for Python code generation using advanced techniques such as QLoRA, gradient checkpointing, and supervised ...

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for training machine learning models like neural networks while ensuring privacy. It modifies the standard gradient descent ...

Abstract: This paper presents an innovative algorithm that combines mini-batch gradient descent with adaptive techniques to enhance the accuracy and efficiency of localization in complex environments.