Fundamentals of Deep Learning

Professor Khanh discusses the fundamentals of deep learning in this session. Three key topics regarding deep learning are highlighted.

Firstly, the need for deep learning arises from the abundance of big data. As shown in the figures, the amount of data has significantly increased, making deep learning popular due to its ability to handle large datasets and improve AI model performance.

Secondly, traditional machine learning algorithms fail to perform well with extensive data, whereas deep learning models excel in such scenarios. Therefore, the utilization of deep learning is crucial when dealing with substantial data.

Finally, the major distinction between traditional machine learning and deep learning lies in the feature engineering step. Traditional machine learning requires extracting features from raw data, while deep learning can automatically extract features through deep neural network-based representation learning.