Artículos relacionados a Introduction to Deep Learning (The MIT Press)
Sinopsis
A project-based guide to the basics of deep learning.
This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach.
Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Eugene Charniak is Professor of Computer Science at Brown University. He is the author of Statistical Language Learning (MIT Press) and other books.
"Sobre este título" puede pertenecer a otra edición de este libro.
Resultados de la búsqueda para Introduction to Deep Learning (The MIT Press)
Imagen de archivo
Introduction to Deep Learning (The MIT Press)
Publicado por
The MIT Press, 2019
ISBN 10: 0262039516
ISBN 13: 9780262039512
Antiguo o usado
Tapa dura
Librería: HPB-Red, Dallas, TX, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! Nº de ref. del artículo: S_472282499
Comprar usado
EUR 5,05
Envío por EUR 3,34
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Introduction to Deep Learning
Publicado por
MIT Press Ltd, 2019
ISBN 10: 0262039516
ISBN 13: 9780262039512
Antiguo o usado
Tapa dura
Librería: World of Books (was SecondSale), Montgomery, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Hardback. Condición: Good. A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. "I find I learn computer science material best by sitting down and writing programs," the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference. Nº de ref. del artículo: 00110143333
Comprar usado
EUR 8,47
Gastos de envío gratis
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Introduction to Deep Learning
Publicado por
MIT Press Ltd, 2019
ISBN 10: 0262039516
ISBN 13: 9780262039512
Antiguo o usado
Tapa dura
Librería: World of Books (was SecondSale), Montgomery, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Hardback. Condición: Very Good. A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. "I find I learn computer science material best by sitting down and writing programs," the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference. Nº de ref. del artículo: 00108358652
Comprar usado
EUR 8,47
Gastos de envío gratis
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Introduction to Deep Learning (Mit Press)
Publicado por
The MIT Press, 2019
ISBN 10: 0262039516
ISBN 13: 9780262039512
Antiguo o usado
Tapa dura
Librería: Bellwetherbooks, McKeesport, PA, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
hardcover. Condición: Very Good. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting. Nº de ref. del artículo: mon0000005598
Comprar usado
EUR 5,05
Envío por EUR 3,52
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Introduction to Deep Learning
Librería: Better World Books: West, Reno, NV, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good. Nº de ref. del artículo: 69034095-75
Comprar usado
EUR 9,44
Gastos de envío gratis
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Introduction to Deep Learning
Publicado por
MIT Press Ltd, 2019
ISBN 10: 0262039516
ISBN 13: 9780262039512
Antiguo o usado
Tapa dura
Librería: World of Books Inc, Montgomery, IL, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Hardback. Condición: Good. A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. "I find I learn computer science material best by sitting down and writing programs," the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference. Nº de ref. del artículo: CIN0262039516G
Comprar usado
EUR 10,31
Gastos de envío gratis
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Introduction to Deep Learning
Publicado por
MIT Press Ltd, 2019
ISBN 10: 0262039516
ISBN 13: 9780262039512
Antiguo o usado
Tapa dura
Librería: World of Books Inc, Montgomery, IL, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Hardback. Condición: Very Good. A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. "I find I learn computer science material best by sitting down and writing programs," the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference. Nº de ref. del artículo: CIN0262039516VG
Comprar usado
EUR 10,31
Gastos de envío gratis
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Introduction to Deep Learning (Mit Press)
Publicado por
The MIT Press, 2019
ISBN 10: 0262039516
ISBN 13: 9780262039512
Antiguo o usado
Tapa dura
Librería: Textbooks_Source, Columbia, MO, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
hardcover. Condición: Good. Illustrated. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes). Nº de ref. del artículo: 002325205U
Comprar usado
EUR 13,41
Envío por EUR 3,55
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 16 disponibles
Imagen de archivo
Introduction to Deep Learning (Mit Press)
Publicado por
The MIT Press, 2019
ISBN 10: 0262039516
ISBN 13: 9780262039512
Antiguo o usado
Paperback
Librería: WorldofBooks, Goring-By-Sea, WS, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Nº de ref. del artículo: GOR011602784
Comprar usado
EUR 26,22
Envío por EUR 6,72
Se envía de Reino Unido a Estados Unidos de America
Se envía de Reino Unido a Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Introduction to Deep Learning (Mit Press)
Librería: GoldBooks, Denver, CO, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: new. New Copy. Customer Service Guaranteed. Nº de ref. del artículo: 15A54_28_0262039516
Comprar nuevo
EUR 29,33
Envío por EUR 4,90
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 1 disponibles