An Introduction to Deep Learning
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Description
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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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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Product details
- Paperback | 220 pages
- 178 x 232 x 18mm | 411g
- 28 Oct 2018
- Pearson Education (US)
- Addison-Wesley Educational Publishers Inc
- New Jersey, United States
- English
- 0135116538
- 9780135116531
