
With the arrival of the big data era and the rapid advancement of computing power, artificial intelligence (AI) technology is changing our world at an unprecedented speed. Among the many branches of AI, machine learning and deep learning not only endow computers with learning capabilities but also enable machines to tackle complex problems like humans, even surpassing human intelligence in certain fields. However, despite their close relationship, there are significant differences in their definitions, technical foundations, data requirements, application areas, and model complexities.
1. Definitions of Machine Learning and Deep Learning

2. Differences Between Machine Learning and Deep Learning

1. Definitions and Technical Foundations
Machine Learning: A broad data analysis technique that enables computers to learn and make decisions automatically through algorithms. It includes various algorithms and methods such as decision trees, SVM, and logistic regression.
Deep Learning: A subset of machine learning that focuses on learning and prediction using deep neural networks. It primarily relies on deep neural network models such as CNN, RNN, and LSTM.
2. Data Requirements
Machine Learning: Has relatively flexible data requirements, with some algorithms performing well on small datasets.
Deep Learning: Requires a large amount of data to achieve excellent results, especially when dealing with complex data.
3. Model Complexity
3. Connections Between Machine Learning and Deep Learning

1. Artificial Intelligence Technology
2. Large Datasets
3. Statistical Foundations
