Machine Learning vs Deep Learning: Understanding the Differences

Artificial intelligence has become a ubiquitous term in today's technological landscape, with various subfields emerging under its umbrella. Machine learning (ML) and deep learning (DL) are two such areas that have garnered significant attention in recent years. While both are concerned with enabling machines to learn from data, they differ in their approaches and applications.

In this article, we will delve into the differences between machine learning and deep learning, exploring how they fit into the broader category of artificial intelligence. We will also examine some of the key methods and techniques used in each field, as well as their potential applications.

What is Machine Learning?

Machine learning is a type of AI that involves training algorithms on data to enable them to make predictions or take actions without being explicitly programmed. It operates at the human abstraction level, where the world is described by entities, concepts, and their mutual relationships. This means that ML models can learn from complex datasets and identify patterns, trends, and correlations.

Some common techniques used in machine learning include embedding approaches, graph neural networks, scene graph analysis, neuro-symbolic programming, and inductive logic programming. These methods enable ML algorithms to extract statistical and logical regularities from data, making them useful for applications such as natural language processing (NLP), computer vision, and recommender systems.

What is Deep Learning?

Deep learning, on the other hand, is a subfield of machine learning that involves training neural networks with multiple layers to analyze and interpret complex data. These networks are designed to mimic the human brain's structure and function, allowing them to learn and recognize patterns in large datasets.

Some common techniques used in deep learning include convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers. These methods enable DL algorithms to extract features from data and make predictions or take actions with high accuracy.

So, what are the key differences between machine learning and deep learning? Here are a few:

* Level of Abstraction: ML operates at the human abstraction level, while DL operates at a more abstract level, focusing on extracting features from data.

* Approach: ML uses a variety of techniques to analyze data, including graph neural networks and inductive logic programming. DL, on the other hand, relies heavily on neural networks with multiple layers.

* Applications: ML is useful for applications such as NLP, computer vision, and recommender systems, while DL is often used for applications such as image recognition, speech recognition, and natural language processing.

In conclusion, machine learning and deep learning are two distinct areas of artificial intelligence that have different approaches, methods, and applications. While ML operates at the human abstraction level and uses a variety of techniques to analyze data, DL focuses on extracting features from data using neural networks with multiple layers. Understanding the differences between these two fields is essential for developing effective AI solutions that can tackle complex problems in various domains.

References

* '1' ELLIS Workshop on Semantic, Symbolic and Interpretable Machine Learning (2024)

* '2' Oberwolfach Research Institute for Mathematics (MFO) (2024)

Machine learning vs deep learning