This course covers the fundamentals of deep learning, including convolutional and recurrent neural networks, and their applications in computer vision, natural language processing, and robotics.
This article presents an overview of recent advances in neural network architectures, including residual networks, attention mechanisms, and graph neural networks, and their applications in various domains.
This online course provides an introduction to advanced neural network models, including autoencoders, generative adversarial networks, and transformers, and their applications in machine learning and AI.
This book provides a comprehensive overview of advanced neural network models for natural language processing, including recurrent neural networks, long short-term memory networks, and transformer models.
This toolbox provides a comprehensive set of tools for designing, training, and deploying neural networks, including advanced models such as convolutional neural networks and recurrent neural networks.
This conference proceedings presents the latest research in neural information processing systems, including advances in deep learning, reinforcement learning, and neural network architectures.
This tutorial provides an introduction to machine learning with advanced neural networks, including TensorFlow and Keras, and their applications in computer vision, natural language processing, and robotics.
This book provides a practical guide to deep learning for computer vision with Python, including advanced neural network models such as convolutional neural networks and recurrent neural networks.