Abstract: Real-time minimization of line loss is a great challenge for conventional distributed control methods in medium-voltage DC distribution system (MVDC-DS), which may lead to low efficiency and ...

Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and ...

Ayyoun is a staff writer who loves all things gaming and tech. His journey into the realm of gaming began with a PlayStation 1 but he chose PC as his platform of choice. With over 6 years of ...

Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning. Michigan couple charged with making millions off hiring illegal immigrants Valerie ...

The first chapter of Neural Networks, Tricks of the Trade strongly advocates the stochastic back-propagation method to train neural networks. This is in fact an instance of a more general technique ...

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 ...

Language-based agentic systems represent a breakthrough in artificial intelligence, allowing for the automation of tasks such as question-answering, programming, and advanced problem-solving. These ...

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.

Abstract: Gradient descent is a fundamental optimization algorithm widely used in artificial intelligence to minimize the loss function and find the optimal parameters of a model, so optimize the ...

ABSTRACT: Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of ...