Embeddings play a crucial role in simplifying complex, high-dimensional data by transforming it into a more manageable, lower-dimensional form. This approach is especially useful for natural language processing (NLP) and recommendation systems where data often contains categorical variables or sequences. PyTorch provides tools to create and work with embeddings that facilitate faster training and improved … Continue reading Embeddings in PyTorch: When to Use and How They Power Machine Learning Models