Machine learning and deep learning are both subfields of artificial intelligence (AI) that involve the use of algorithms to learn from data. However, there are some key differences between the two.
Machine learning is a method of teaching computers to perform tasks without explicitly programming them. It involves the use of algorithms that can learn from data and improve their performance over time. There are several types of machine learning, including supervised learning, in which the algorithm is trained on a labeled dataset, and unsupervised learning, in which the algorithm is not given any labels.
Deep learning is a type of machine learning that involves the use of artificial deep neural networks, which are artificial neural networks with a large number of layers inspired by the structure and function of the human brain. These neural networks are made up of layers of interconnected nodes, and they are able to learn and make decisions by analyzing large amounts of data. Deep learning algorithms are particularly good at recognizing patterns and making decisions based on that data.
The term "deep" in deep learning refers to the depth of the neural network, which refers to the number of layers it has. Neural networks with a large number of layers are able to learn and make decisions based on more complex patterns in the data, which makes them more powerful and effective for tasks such as image and speech recognition.
Deep learning algorithms are able to learn from large amounts of data and can recognize patterns that are not easily detectable by humans or traditional machine learning algorithms. This makes them particularly useful for tasks such as image and speech recognition, where there is a large amount of data and a need to recognize complex patterns.
