The pitfalls of Deep Learning - When Neural Networks are not the solution

Netflix's prize-winning algorithm was never used because it was too complex. Learn when a simpler machine learning model is the smarter business decision.

#1about 2 minutes

Defining classical machine learning vs deep learning

Classical machine learning relies on manual feature extraction, whereas deep learning models automate this process to find representations directly from data.

#2about 3 minutes

Highlighting successful applications of deep learning

Deep learning excels in complex domains like self-driving cars, language translation, and music generation due to its powerful representation learning capabilities.

#3about 3 minutes

Examining notable failures of deep learning models

Real-world deep learning failures include biased facial recognition systems, contextual mistranslations, and pseudoscientific claims about predicting personal traits.

#4about 6 minutes

The critical role of data quantity and quality

Deep learning models require vast amounts of high-quality, relevant data to learn effective features, as insufficient or poor data leads to unreliable performance.

#5about 3 minutes

Why tree-based models often outperform deep learning on tabular data

For structured tabular data common in business, tree-based models like LightGBM and XGBoost frequently outperform deep learning due to effective feature engineering.

#6about 4 minutes

The challenge of model explainability in deep learning

Deep learning's automatic feature extraction creates black-box models, making it difficult to understand decision-making compared to interpretable classical models like decision trees.

#7about 2 minutes

Navigating model complexity and production engineering costs

Highly complex models, like the Netflix Prize winner, can be impractical to deploy due to high engineering costs and resource requirements.

#8about 4 minutes

The significant resource and financial cost of training

Training state-of-the-art deep learning models requires immense computational resources, potentially costing millions of dollars and making it inaccessible for many organizations.

#9about 4 minutes

Strategies to overcome deep learning limitations

Techniques like transfer learning, emerging tabular deep learning methods, and interpretability tools like LIME and SHAP help mitigate issues of data, cost, and explainability.

#10about 1 minute

Deciding when to choose classical machine learning

Before adopting deep learning, evaluate if your problem involves small, tabular, or low-quality data, as classical machine learning may offer a more practical solution.