03128nam a22003854u 450000100220000000300060002200500170002800700150004500800410006002000300010102400310013104100080016210000330017024501280020325000060033126000160033726400160035330000120036933600260038133700260040733800360043334700210046934700080049052013990049865000200189765000280191765000330194565303650197870000220234370000330236577600360239885600640243485601020249885601420260010.1002/9781119861898Wiley20251030120307.0cr nn 008mam|a240705t2024||||xxu    fs|   |0|| 0|eng||  a9781119861898qelectronic7 a10.1002/97811198618982doi  aeng1 aMartinez‐Ramon, Manel4aut10aDeep LearningbA Practical Introductionh[electronic resource]cManel Martinez‐Ramon, Meenu Ajith, Aswathy Rajendra Kurup  a1  bWileyc2024 1bWileyc2024  a416pp.;  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext file2rdaft  bPDF  aAn 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. 4aNeural Networks 4aArtificial Intelligence 4aAlgorithms & Data Structures00aDeep 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; DBN1 aAjith, Meenu4aut1 aKurup, Aswathy Rajendra4aut08iPrinted edition:z978111986186740uhttps://doi.org/10.1002/9781119861898aWiley Online Library40uhttps://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781119861898aWiley Online Library423Cover Imageuhttps://onlinelibrary.wiley.com/cms/asset/7ef58906-d1ed-41b7-b188-427c1f2bca2c/9781119861898.cover.gifaWiley Online Library