05 Oct AI vs ML vs DL
We learned about Deep Learning in the previous lessons. Let us see the difference between Artificial Intelligence, Machine Learning, and Deep Learning.
| Artificial Intelligence (AI) | Machine Learning (ML) | Deep Learning (DL) | |
|---|---|---|---|
| What? | Artificial Intelligence means creating smart machines to mimic human behavior | In Machine Learning, the computer is fed with data and information. This gives these systems the ability to learn and enhance from experiences. | Deep Learning uses neural networks to model and solve complex problems |
| Subset/ Superset | AI is a superset of both ML and DL | ML is a subset of AI. | DL is a subset of ML. |
| Easy or Complex | Easier rules to advanced algorithms | Uses algorithms like decision trees, SVM, etc. | Uses complex neural networks with many layers. |
| Data | Can work with structured data and predefined rules | Needs structured data for training | Needs large amounts of labeled data for effective training |
| Human | |||
| Intervention | Human Intervention is high, especially in rule-based systems | Human Intervention is moderate, requires feature extraction | Human Intervention is low, since it has automatic feature extraction |
| Hardware Requirements | Generally lower, depends on application | Moderate, can run on standard hardware | High, requires GPUs for training deep networks. Modern GPUs (Graphics Processing Units) are used for parallel computation. |
| Applications/ Uses | Robotics, game playing, etc. | Email filtering, healthcare, recommendation systems, etc. | Image and speech recognition, language processing, etc. |
| Efficiency | Varies based on complexity and design | Generally good with sufficient data | Often superior performance with large datasets |
| Learning Techniques | Rule-based learning, expert systems, etc. | Supervised, unsupervised, and reinforcement learning | Primarily supervised and unsupervised learning with deep networks |
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