03245nam a22004094u 4500 10.1002/9781119861898 Wiley 20251030120307.0 cr nn 008mam|a 240705t2024||||xxu fs| |0|| 0|eng|| 9781119861898 electronic 10.1002/9781119861898 doi eng Martinez‐Ramon, Manel aut 91500 Deep Learning A Practical Introduction [electronic resource] Manel Martinez‐Ramon, Meenu Ajith, Aswathy Rajendra Kurup 1 Wiley 2024 Wiley 2024 416pp.; text txt rdacontent computer c rdamedia online resource cr rdacarrier text file rdaft PDF An engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning tools Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures Practical discussions of recurrent neural networks and non-supervised approaches to deep learning Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general. Neural Networks 91501 Artificial Intelligence 58441 Algorithms & Data Structures 91502 Deep learning; machine learning; neural networks; convolutional neural networks; recurrent neural networks; long short-term memory; transformers; autoencoders; deep belief networks; generative adversarial networks; deep Bayesian networks; deep gaussian processes; deep learning examples; Python; Pytorch; Keras; Tensorflow; DL; ML; NN; CNN; RNN; LSTM; GAN; DBN Ajith, Meenu aut 91503 Kurup, Aswathy Rajendra aut 91504 Printed edition: 9781119861867 https://doi.org/10.1002/9781119861898 Wiley Online Library https://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781119861898 Wiley Online Library Cover Image https://onlinelibrary.wiley.com/cms/asset/7ef58906-d1ed-41b7-b188-427c1f2bca2c/9781119861898.cover.gif Wiley Online Library 66979 66979