What are the main challenges faced by RNNs during training, and how do Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) address these issues?
Recurrent Neural Networks (RNNs) are a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows them to exhibit temporal dynamic behavior and make them suitable for tasks involving sequential data such as time series prediction, natural language processing, and speech recognition. Despite their potential, RNNs
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Recurrent neural networks, Sequences and recurrent networks, Examination review
What are some potential issues that can arise with neural networks that have a large number of parameters, and how can these issues be addressed?
In the field of deep learning, neural networks with a large number of parameters can pose several potential issues. These issues can affect the network's training process, generalization capabilities, and computational requirements. However, there are various techniques and approaches that can be employed to address these challenges. One of the primary issues with large neural