An Introduction to Deep Learning is the complete guide to writing deep learning programs with the widely-used Python language and TensorFlow programming environment. Building on his pioneering undergraduate and graduate courses, Brown University professor Eugene Charniak covers every key concept and technique, including feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and more. Each chapter ...
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An Introduction to Deep Learning is the complete guide to writing deep learning programs with the widely-used Python language and TensorFlow programming environment. Building on his pioneering undergraduate and graduate courses, Brown University professor Eugene Charniak covers every key concept and technique, including feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and more. Each chapter contains a full programming project and a set of exercises carefully crafted to help readers build mastery, as well as additional readings and references for even deeper insight.
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Add this copy of Introduction to Deep Learning (Mit Press) to cart. $16.91, good condition, Sold by SurplusTextSeller rated 5.0 out of 5 stars, ships from Columbia, MO, UNITED STATES, published 2019 by MIT Press.
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