Data collection is the process of gathering and formatting information in a way that allows us to develop a practical machine learning model. Data is collected from many different sources, and proper data collection is one of the most important parts of developing an effective machine learning model.
A artificial neural network is a system of algorithms inspired by the biological neural networks in our own brains. In practice, a set of nodes, or neurons, interact with each other through a series of inputs and outputs along edges that connect them. Used properly, an artificial neural network can be a powerful tool in developing machine learning models.
Deep Learning is a subset of machine learning in which complex neural networks are used to learn on data. Deep learning models are unsupervised, meaning that the data is unstructured or unlabeled. This allows a deep learning model to create features in ways unknown or not understood by computer scientists.
John Gauthier: CSE Major
Sam Kasbawala: CSE Major
Mickey Mannella: CSE Major
Ryan Miller: CSE Major