Is Machine Learning and Deep Learning the same? | Article – HSBC VisionGo
Definitions of Machine Learning (ML) and Deep Learning (DL)Machine learning is an important branch of artificial intelligence (AI). It focuses on imitating the way human learns with the use of data and algorithms. In machine learning, machines can gradually improve their accuracy in decision making, just like humans. To clarify, Deep Learning should not be used interchangeably with Machine Learning. It is because Deep Learning does not precisely equal Machine Learning. Deep learning is a sub-field of Machine Learning.
Key difference between machine learning and deep learning
The key difference between deep learning and machine learning is how the algorithm learns. In classical machine learning, machines depend more on human intervention to learn. The engineers predefine the set of features for the AIs to classify data.
On the other hand, in deep learning, learning can be automated. The algorithm can automatically determine the set of features to distinguish data. The algorithm accepts all sorts of data, regardless of labelled or unlabeled. It can even ingest unstructured data in raw form (e.g. images, videos, texts). The intense learning power of deep learning comes from implementing artificial neural networks, which comprises complicated layers of algorithms. The artificial neural network is inspired and designed based on the biological neural network. To add on, the "deep" in deep learning actually refers to the depth of layers in a neural network.
Machine learning methods
Machine learning methods can be classified into four major types: supervised, unsupervised, semi-supervised, and reinforcement.
1. Supervised learning
Supervised learning uses labelled datasets to train AI models. The labelled datasets consist of inputs and outputs. The goal in supervised learning is to generate functions that can map inputs to the desired outputs. In the end, the algorithms can classify data and predict outcomes...
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