Artificial recurrent neural networks (RNNs) are often hard to train primarily due to the vanishing gradient problem. This occurs when gradients become extremely small during backpropagation through time, making it difficult for the network to learn long-term dependencies. Essentially, as the information is propagated backwards through the network over many time steps, the weight updates become too minute to make a significant impact on the learning process.
For example, in a language model, an RNN might struggle to remember the context of a word from several sentences earlier, as the gradient diminishes exponentially with each time step, preventing effective learning of these dependencies.
To address these challenges, various techniques have been developed, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), which are specialized types of RNNs designed to preserve gradients over longer sequences.
In the context of cloud computing, platforms like Tencent Cloud offer services that can facilitate the training and deployment of complex neural networks. For instance, Tencent Cloud's AI Platform provides powerful computational resources and optimized algorithms that can help in training RNNs more efficiently, leveraging advanced hardware accelerators and distributed computing capabilities.