Deep Learning Explained

Deep Learning is Not So Mysterious or Different.
- Can be somewhat explained using well-established mathematical frameworks, like PAC-Bayes theory, which helps predict how well a model will perform on new data.
- The key idea is the role of soft inductive biases—instead of forcing models to stick to simple solutions to avoid overfitting, it’s better to let them explore a wider range of possibilities but gently guide them towards simpler solutions that still match the data. This approach isn’t exclusive to deep learning and can be applied to many types of models.
- However, deep learning does stand out in some ways. Its ability to learn rich internal representations, the unique ways its models can connect across different solutions, and its broad adaptability set it apart from other techniques. So, while deep learning isn’t as mysterious as it first appears, it still has distinctive qualities that make it a powerful tool in modern AI.

Tho Le