Question

How is a Machine Learning (ML) system different from a Deep Learning (DL) system?

A.A ML system needs a human to provide the feature extraction.
B.A ML system does all of the feature extraction and classification.
C.A ML system learns from its mistakes.

Answer 1

A Machine Learning (ML) system requires human intervention for feature extraction, while a Deep Learning (DL) system can automatically extract features from raw data. DL systems have more complex learning capabilities, thanks to their multiple interconnected layers of artificial neurons.

Step-by-step explanation:

A Machine Learning (ML) system and a Deep Learning (DL) system are both examples of Artificial Intelligence (AI) techniques used to train computer systems to learn and make predictions. However, there are some key differences between them.

In an ML system, a human is typically responsible for providing the feature extraction. This means that the human needs to identify and extract the relevant features from the data before feeding it into the ML system. On the other hand, a DL system has the ability to automatically extract features from the raw data, eliminating the need for human intervention in feature extraction.

Additionally, while both ML and DL systems learn from data, they differ in the complexity and depth of their learning. A DL system consists of multiple layers of interconnected artificial neurons that can mimic the structure and function of the human brain. This allows DL systems to learn complex patterns and relationships in data, leading to superior performance in tasks such as image recognition or natural language processing. In contrast, an ML system typically applies simpler algorithms and techniques to learn from data, making it more suitable for tasks with less complex patterns.