No, deep learning is not just overfitting, though overfitting can occur if models are not trained and validated properly. Overfitting happens when a model learns the noise or specific details of the training data instead of general patterns, leading to poor performance on unseen data. However, modern deep learning practices include techniques to mitigate overfitting, such as regularization, dropout, and data augmentation. Deep learning has demonstrated its ability to generalize and perform well across diverse applications, such as image classification, natural language processing, and reinforcement learning. Models like ResNet, GPT, and YOLO have shown exceptional accuracy and scalability, proving that deep learning can handle complex tasks effectively. While deep learning models can be prone to overfitting without careful design, the field has developed robust methods to address this issue, enabling reliable and accurate results in real-world applications.
Is deep learning just overfitting?

- GenAI Ecosystem
- Exploring Vector Database Use Cases
- Large Language Models (LLMs) 101
- Getting Started with Zilliz Cloud
- Vector Database 101: Everything You Need to Know
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How does observability handle caching layers in databases?
Observability in the context of databases, particularly with caching layers, involves monitoring and understanding how c
How do you create a temporary table in SQL?
Creating a temporary table in SQL is a straightforward process that allows you to hold data temporarily during a databas
How does edge AI reduce the need for cloud data centers?
Edge AI reduces the need for cloud data centers primarily by processing data closer to where it is generated, rather tha