Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

Deep learning is at the core of the large language models used by OpenAI's ChatGPT and Microsoft Copilot, for example. More specialized deep learning models have supported a wide range of scientific ...

College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China Introduction: With the rapid development of 5G technology, Mobile Edge Computing (MEC) has become a ...

SnakeRL is a Python project implementing the Snake game with Deep Q-Learning. The agent learns to navigate, collect food, and avoid collisions using a neural network, dynamic rewards, and customizable ...

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3 ...

Sign up for the daily CJR newsletter. In the fall of 1967, during the presidency of Lyndon B. Johnson, whispers began to swirl in Washington that a shocking ...

Abstract: A Deep Q-Learning approach to Intrusion Detection and Prevention Systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models.

ABSTRACT: Diabetic retinopathy (DR), a leading cause of vision impairment worldwide, primarily impacts individuals with diabetes, making early detection vital to prevent irreversible vision loss.

Aiming at the problems of slow network convergence, poor reward convergence stability, and low path planning efficiency of traditional deep reinforcement learning algorithms, this paper proposes a ...