000			01714 a2200217 4500
003			OSt
020			_a9781108835084
040			_cIIT Kanpur
041			_aeng
082			_a006.31 _bD837s
100			_aDrori, Iddo
245			_aThe science of deep learning _cIddo Drori
260			_bCambridge University Press _c2023 _aCambridge
300			_axxii, 338p
520			_a The 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			_aCommunications and signal processing
650			_aComputer science engineering
650			_aMachine learning
650			_aPattern recognition
942			_cBK
999			_c566889 _d566889