How Do Machine Learning and Deep Learning Differ?
As mentioned in previous lessons, deep learning is a subset of machine learning. Let's explore the key differences between machine learning and deep learning through the table below.
| Machine Learning | Deep Learning | |
|---|---|---|
| Definition | A branch of artificial intelligence that creates predictive models by learning from data. | A subset of machine learning that uses artificial neural networks to learn from data and create predictive models. |
| Algorithms | Regression, classification, clustering, decision trees, SVM, etc. | CNN, RNN, LSTM, GAN, etc. |
| Model Structure | Relatively simple model structures used. | Complex multi-layer neural network structures used. |
| Learning Methods | Supervised learning, unsupervised learning, reinforcement learning. | Mostly supervised, unsupervised learning available. |
| Data Requirement | Can learn from relatively small amounts of data. | Requires large amounts of data. |
| Computing Resources | Requires relatively fewer computing resources. | Requires substantial computing resources like high-performance GPUs. |
| Applications | Recommendation systems, financial forecasting, disease diagnosis, etc. | Image recognition, natural language processing, autonomous driving, etc. |
| Learning Speed | Can learn relatively quickly. | May be slow due to large datasets and complex models. |
Machine learning uses relatively simple models that can be applied to various fields with smaller amounts of data and computational resources. In contrast, deep learning leverages complex neural networks that require large amounts of data and high-performance computing resources, excelling in high-dimensional tasks such as image recognition.
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