Ques:-- What is the difference between machine learning and deep learning?
Ans: -- Machine learning and deep learning are two related but distinct fields within artificial intelligence (AI). Both use algorithms to analyse and learn from data, but they differ in their approaches and the types of data they can process.
Machine learning involves the creation of models that can learn from data and improve over time. These models are based on statistical algorithms that are trained using historical data to make predictions or take actions without being explicitly programmed. Machine learning algorithms can be supervised, unsupervised, or semi-supervised, and can be used for a variety of tasks, such as image and speech recognition, natural language processing, and fraud detection.
Deep learning, on the other hand, is a more specialised type of machine learning that uses artificial neural networks (ANNs) to learn from large, unstructured data sets. These networks are composed of many layers of interconnected nodes that can process complex data sets with multiple features. Deep learning algorithms can learn to recognise patterns and features in data, and can be used for tasks such as image and speech recognition, natural language processing, and autonomous vehicles.
The main difference between machine learning and deep learning is that machine learning uses algorithms that are designed to work with structured data sets, while deep learning algorithms can work with unstructured data sets. Machine learning models typically require feature engineering, which involves selecting and transforming relevant features from the data, while deep learning algorithms can learn to identify relevant features on their own.
Another key difference between the two is the amount of data required for training. Machine learning algorithms can work with smaller data sets, while deep learning algorithms require large amounts of data to train effectively. This is because ANNs are composed of many layers of nodes, and each node learns from the previous layer, which requires a lot of data to create an effective model.
In summary, machine learning and deep learning are both important subfields within AI that use algorithms to analyse and learn from data. While machine learning is more general and can work with structured data sets, deep learning is more specialised and can work with unstructured data sets, but requires large amounts of data to train effectively.
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