For what use cases do you think deep learning and neural networks overkill and simpler models work better in practice?

Deep learning and neural networks are powerful tools for complex tasks, but they can be overkill in certain scenarios where simpler models suffice or even perform better. Here are some use cases where simpler models might be more appropriate:

1. Small Datasets

• Use Case: When dealing with small datasets, deep learning models often struggle due to overfitting. Simpler models like linear regression, decision trees, or logistic regression can handle these datasets more effectively.
• Reason: Deep learning requires large amounts of data to train effectively. With limited data, simpler models can generalize better without overfitting.

2. Simple Pattern Recognition

• Use Case: Tasks that involve recognizing simple patterns or making straightforward classifications.
• Reason: Simple neural networks or even non-neural network methods like k-NN or Naive Bayes can perform well in these scenarios without the need for complex deep architectures.

3. Interpretable Models

• Use Case: Situations where model interpretability is crucial, such as in some financial or medical applications.
• Reason: Simpler models like linear regression or decision trees are more interpretable than deep learning models, which can be black boxes.

4. Low Computational Resources

• Use Case: Projects with limited computational resources or real-time constraints.
• Reason: Deep learning models require significant computational power and time to train. Simpler models are faster to train and deploy.

5. Class Imbalance Problems

• Use Case: Handling datasets with severe class imbalance.
• Reason: In some cases, simpler models combined with techniques like oversampling the minority class or using class weights can perform better than deep learning models that might overfit the majority class.

6. Rule-Based Systems

• Use Case: Applications where rules are well-defined and straightforward.
• Reason: Rule-based systems can be more efficient and transparent than deep learning models in scenarios where the decision-making process is well-understood and doesn't require complex pattern recognition.

Examples of Successful Simpler Models

• Pharmaceutical Next Best Action Algorithms: Simpler models might be preferred for their interpretability and efficiency in decision-making processes.
• Financial Technology NLP Tasks: Traditional NLP methods like TF-IDF with linear SVM can outperform deep learning models when data is limited or when simplicity is preferred.
• Recommender Systems: Collaborative filtering combined with algorithms like LightGBM can be more effective than deep learning in certain scenarios, especially when data quality is not optimal.

In summary, while deep learning is powerful, it's not always the best choice. Simpler models can offer better performance, interpretability, and efficiency in specific use cases.