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Название: AI Literacy Fundamentals
Автор: Ben Jones
Издательство: Data Literacy Press
Год: 2024
Страниц: 240
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
Feeling overwhelmed by AI? It's not you—it's the breakneck speed of technological progress. To quickly get into the AI conversation, you need a clear and simple foundation of knowledge to build on. This book is a friendly primer on the basic concepts of AI, how it's already snuck into our daily lives, and what we need to know to prepare for the future. Machine Learning (ML) is an umbrella term describing the study and use of different types of statistical algorithms that we can apply to data sets in order to learn from the patterns in those data sets how to accomplish specific tasks. When we apply a Machine Learning algorithm to a data set to learn its patterns, we say that we are training a model. Deep Learning is a special branch of Machine Learning that involves a specific family of artificial neural networks: deep neural networks (DNN). DNNs have changed the world of AI, and in so doing they have changed the world we live in. Any neural network that contains two or more hidden layers is considered a deep neural network. Unsurprisingly, then, a neural network with a single hidden layer is sometimes referred to as a shallow neural network. Furthermore, the number of hidden layers corresponds to the depth of the deep neural network. To wrap up our own study of Deep Learning and neural networks, I’d like to instead consider a few different types of deep neural networks that have led to breakthrough capabilities in the fields of computer vision and natural language processing. The first type is the convolutional neural network (CNN), sometimes called the ConvNet.
Автор: Ben Jones
Издательство: Data Literacy Press
Год: 2024
Страниц: 240
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
Feeling overwhelmed by AI? It's not you—it's the breakneck speed of technological progress. To quickly get into the AI conversation, you need a clear and simple foundation of knowledge to build on. This book is a friendly primer on the basic concepts of AI, how it's already snuck into our daily lives, and what we need to know to prepare for the future. Machine Learning (ML) is an umbrella term describing the study and use of different types of statistical algorithms that we can apply to data sets in order to learn from the patterns in those data sets how to accomplish specific tasks. When we apply a Machine Learning algorithm to a data set to learn its patterns, we say that we are training a model. Deep Learning is a special branch of Machine Learning that involves a specific family of artificial neural networks: deep neural networks (DNN). DNNs have changed the world of AI, and in so doing they have changed the world we live in. Any neural network that contains two or more hidden layers is considered a deep neural network. Unsurprisingly, then, a neural network with a single hidden layer is sometimes referred to as a shallow neural network. Furthermore, the number of hidden layers corresponds to the depth of the deep neural network. To wrap up our own study of Deep Learning and neural networks, I’d like to instead consider a few different types of deep neural networks that have led to breakthrough capabilities in the fields of computer vision and natural language processing. The first type is the convolutional neural network (CNN), sometimes called the ConvNet.