Artificial Intelligence (AI) is the branch of computer science that aims to create machines that can perform tasks that would typically require human intelligence, such as recognizing speech, understanding natural language, making decisions, and playing games.
Machine Learning (ML) is a subset of AI that involves training computer models to make predictions or take actions without being explicitly programmed. The models are trained using a set of input-output pairs, which allows them to learn how to map inputs to outputs.
Deep Learning (DL) is a type of ML that involves training neural networks, which are composed of layers of interconnected nodes, or “neurons.” These networks are able to learn hierarchical representations of the input data, which makes them well-suited for tasks such as image and speech recognition, natural language processing, and video analysis.
One key difference between traditional machine learning algorithms and deep learning algorithms is the depth of the models. Traditional machine learning algorithms are usually shallow, with one or two layers of neurons, while deep learning algorithms are made up of multiple layers. This allows deep learning algorithms to learn more complex representations of the data, which in turn improves their performance on a wide range of tasks.
Deep learning models are trained using large amounts of data and powerful computing resources. One popular method for training deep learning models is called stochastic gradient descent, which involves updating the model’s parameters in order to minimise the error on the training data.
In summary, AI is a broad field of computer science that aims to create machines with human-like intelligence. Machine learning is a subset of AI that involves training computer models to make predictions or take actions, and deep learning is a type of ML that uses deep neural networks for feature learning and representation.