In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine ...

This framework provides a comprehensive set of tools and utilities for implementing and experimenting with Extreme Learning Machines using Python and TensorFlow. ELMs are a type of machine learning ...

Abstract: This paper introduces Q-learning with gradient target tracking, a novel reinforcement learning framework that provides a learned continuous target update mechanism as an alternative to the ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

Artificial intelligence (AI) is increasingly prevalent, integrated into phone apps, search engines and social media platforms as well as supporting myriad research applications. Of particular interest ...

Why is this important? This upgrade will allow users to pull source material directly from their Gmail, Drive, or Chat, eliminating the need to manually download and upload files. Why should I care?

Note: minor-version differences between wheel tags, runtime CUDA, nvcc, and the host driver are common. Use the core test logs in ml_env_logs/ as the canonical verification artifacts.

The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception, converging in some areas while ...

Abstract: Non-orthogonal Multiple Access (NOMA) is a crucial technique in Cognitive Radio Networks (CRNs) that improves frequency band use efficiency. However, NOMA may encounter difficulties due to ...