000			a
999			_c32341 _d32341
008			231024b xxu||||| |||| 00| 0 eng d
020			_a9781108835084
082			_a006.31 _bDRO
100			_aDrori, Iddo
245			_aScience of deep learning
260			_bCambridge University Press, _c2023 _aCambridge :
300			_axxii, 338 p. ; _bill., map, _c26 cm.
365			_b44.99 _cGBP _d107.60
504			_aIncludes bibliographical references and index.
520			_aThe Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
650			_aDeep learning
650			_aMachine learning
650			_aArtificial Intelligence
650			_aComputer Vision
650			_aTransformers
650			_aGraph neural networks
650			_aVariational autoencoders
650			_aDeep reinforement
942			_2ddc _cBK