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Show Notes
Deep Learning (Ian Goodfellow)
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- Read more: https://english.9natree.com/read/0262035618/
#backpropagation #convolutionalnetworks #regularization #representationlearning #deepgenerativemodels #DeepLearning
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is a major technical textbook in artificial intelligence and machine learning, published by MIT Press as part of the Adaptive Computation and Machine Learning series. Its purpose is not to teach deep learning through code recipes, but to explain the mathematical, conceptual, and methodological foundations that make modern neural networks work. The book moves from prerequisites such as linear algebra, probability, information theory, and numerical computation to core machine learning principles, then to deep network architectures, optimization, regularization, convolutional networks, sequence models, autoencoders, representation learning, and generative models. It is written for advanced students, researchers, and practitioners who need a rigorous understanding of the field rather than a simplified implementation guide. Its distinctive value lies in combining foundational theory with an organized view of deep learning as both an engineering discipline and a research program.
- Amazon USA Store: https://www.amazon.com/dp/0262035618?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Deep-Learning-Ian-Goodfellow.html
- Apple Books: https://books.apple.com/us/audiobook/deep-learning/id1643323905?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Deep+Learning+Ian+Goodfellow+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/0262035618/
#backpropagation #convolutionalnetworks #regularization #representationlearning #deepgenerativemodels #DeepLearning
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is a major technical textbook in artificial intelligence and machine learning, published by MIT Press as part of the Adaptive Computation and Machine Learning series. Its purpose is not to teach deep learning through code recipes, but to explain the mathematical, conceptual, and methodological foundations that make modern neural networks work. The book moves from prerequisites such as linear algebra, probability, information theory, and numerical computation to core machine learning principles, then to deep network architectures, optimization, regularization, convolutional networks, sequence models, autoencoders, representation learning, and generative models. It is written for advanced students, researchers, and practitioners who need a rigorous understanding of the field rather than a simplified implementation guide. Its distinctive value lies in combining foundational theory with an organized view of deep learning as both an engineering discipline and a research program.
