Advances in Deep Learning: Enhancing Neural Networks for Autonomous Systems in Real-World Applications
Keywords:
Deep learning, neural networks, autonomous systems, convolutional neural networks, reinforcement learning, self-driving cars, robotics, generative adversarial networks, deep reinforcement learning, model interpretabilityAbstract
Deep learning has experienced unprecedented advancements in recent years, significantly impacting various sectors, including autonomous systems. This paper explores the evolution of deep learning algorithms and their integration into autonomous systems, particularly in realworld applications such as self-driving cars, robotics, and drones. The paper reviews critical advances in neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning, and how they have enhanced the performance and safety of autonomous systems. The challenges in deploying these technologies in real-world settings, including data privacy, model interpretability, and system reliability, are also discussed. Finally, future directions for deep learning in autonomous systems are explored, with a focus on scalability, energy efficiency, and ethical considerations.
