Abstract: The proposed system leverages Deep Q-Learning to enhance autonomous vehicle navigation in smart mobility environments. By integrating reinforcement learning with deep neural networks, 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 ...

Learn about DenseNet, one of the most powerful deep learning architectures, in this beginner-friendly tutorial. Understand its structure, advantages, and how it’s used in real-world AI applications.

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 ...

Lucas is a writer and narrative designer from Argentina with over 15 years of experience writing for games and news. He keeps a watchful eye at the gaming world and loves to write about the hottest ...

This project uses Deep Q-Learning to train a Mario agent in a reinforcement learning environment. The agent is optimized using dynamic exploration rates, custom reward shaping, and Prioritized ...

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 ...

Deep learning is an AI function and a subset of machine learning, used for processing large amounts of complex data. Deep learning can automatically create algorithms based on data patterns.