Deep Learning algorithms can create very accurate predictive models on structured databases such as SQL, Excel sheets, etc. Using entity embeddings, deep learning models have outperformed popular gradient boosting models on tabular and time-series datasets.
Neural networks are becoming increasingly popular for tabular data, as they do not require a need for domain expertise. In fact, Google conducts most of their deep learning projects on tabular data. Some of the features of neural networks on tabular data include:
Understanding and predicting consumer’s purchasing patterns
Root-Cause analysis for identification of faults in operations
Accurate classification of categorical data using entity embeddings
Neural Networks can learn convoluted mappings between various inputs and outputs, and hence it can create supervised learning models to understand trends in time-series data. Some of the capabilities of neural networks include:
Automated real-time forecasts of trends and seasonalities
Understanding temporal dependencies between multiple inputs and outputs