One-hot encoding presents two main challenges: high dimensionality, which increases computational time, especially with a large number of categories, and the lack of encoded relationships between categories, making them appear entirely independent. An embedding column addresses these issues by representing each category with a smaller vector of weights (e.g., 5 values), where each value acts as a feature of the category. This allows the neural network to recognize similarities between categories based on their embedding vectors. Reference: Cloud Academy