NLP, Artificial Intelligence, Data Science, Machine Learning, Neural Networks.
parallelism is a very common technique in deep learning in which each input batch of training data is divided among the data-parallel workers. Gradients are then communicated and aggregated among data-parallel workers before updating the model weights. Data parallelism relies on scaling the batch