- Neural networks are a core part of machine learning and are also the foundation of deep learning.
- The distinction between machine learning and deep learning depends on the complexity and depth of the neural network.
Understanding neural networks in machine learning
Neural networks are computational models inspired by the human brain, consisting of interconnected nodes that process information. In the context of machine learning, neural networks are used to recognise patterns, make predictions, and learn from data. Machine learning is a broad field that encompasses a variety of techniques and models, including neural networks.
Machine Learning involves training models to make decisions or predictions based on data. Neural networks are one of the many tools used in machine learning, particularly for tasks that require pattern recognition, such as image classification or speech recognition.
When does a neural network become deep learning?
The concept of deep learning arises when these neural networks have multiple layers (often more than three), allowing them to learn more complex and abstract features from the data. The “depth” refers to the number of layers in the neural network:
Shallow neural networks: These have one or two hidden layers and are typically used in simpler machine learning tasks.
Deep neural networks: These contain multiple hidden layers and are capable of performing more complex tasks. When a neural network has enough depth, it falls under the category of deep learning.
Deep learning is a subset of machine learning focused specifically on using deep neural networks to solve complex problems. It has become particularly prominent in areas like natural language processing, computer vision, and autonomous systems.
Also read: 7 reasons why we use neural networks in machine learning
Also read: The essential role of optimisers in neural networks
Key differences between machine learning and deep learning
Feature engineering: In traditional machine learning, features are often manually extracted from data before being fed into models. Neural networks in deep learning, however, can automatically learn and extract features directly from raw data.
Data requirements: Deep learning models generally require more data to perform well, compared to other machine learning models. This is because the multiple layers in deep neural networks need a lot of data to learn effectively.
Computational power: Deep learning typically requires more computational resources due to the complexity of the models, whereas traditional machine learning models can often be trained on less powerful hardware.
Neural networks serve as a bridge between machine learning and deep learning. While they are fundamental to both, the depth and complexity of the neural network determine whether it’s being used within a traditional machine learning framework or a deep learning one. In essence, all deep learning is machine learning, but not all machine learning involves deep learning.
