{"product_id":"deep-learning-a-practical-introduction-hardback-9781119861867","title":"Deep Learning; A Practical Introduction (Hardback) 9781119861867","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eDeep Learning\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Practical Introduction\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eManel Martinez-Ramon (Author), Meenu Ajith (Author), Aswathy Rajendra Kurup (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119861867, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 8 August 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e416 pages\u003cbr\u003e25.1 x 17.7 x 3.2 cm, 1.007 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eAn engaging and accessible introduction to deep learning perfect for students and professionals\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn \u003ci\u003eDeep Learning: A Practical Introduction\u003c\/i\u003e, 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. \u003c\/p\u003e\n\u003cp\u003eCombining 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: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThorough introductions to deep learning and deep learning tools\u003c\/li\u003e\n\u003cli\u003eComprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures\u003c\/li\u003e\n\u003cli\u003ePractical discussions of recurrent neural networks and non-supervised approaches to deep learning\u003c\/li\u003e\n\u003cli\u003eFulsome treatments of generative adversarial networks as well as deep Bayesian neural networks\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, \u003ci\u003eDeep Learning: A Practical Introduction \u003c\/i\u003ewill also benefit practitioners and researchers in the fields of deep learning and machine learning in general.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eAbout the Authors xv\u003c\/p\u003e \u003cp\u003eForeword xvii\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAcknowledgment xxi\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 The Multilayer Perceptron 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 The Concept of Neuron 2\u003c\/p\u003e \u003cp\u003e1.3 Structure of a Neural Network 14\u003c\/p\u003e \u003cp\u003e1.4 Activations 21\u003c\/p\u003e \u003cp\u003e1.5 Training a Multilayer Perceptron 22\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Training Practicalities 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 41\u003c\/p\u003e \u003cp\u003e2.2 Generalization and Overfitting 42\u003c\/p\u003e \u003cp\u003e2.3 Regularization Techniques 45\u003c\/p\u003e \u003cp\u003e2.4 Normalization Techniques 50\u003c\/p\u003e \u003cp\u003e2.5 Optimizers 52\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Deep Learning Tools 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Python: An Overview 61\u003c\/p\u003e \u003cp\u003e3.2 NumPy 72\u003c\/p\u003e \u003cp\u003e3.3 Matplotlib 83\u003c\/p\u003e \u003cp\u003e3.4 Scipy 97\u003c\/p\u003e \u003cp\u003e3.5 Scikit-Learn 107\u003c\/p\u003e \u003cp\u003e3.6 Pandas 116\u003c\/p\u003e \u003cp\u003e3.7 Seaborn 125\u003c\/p\u003e \u003cp\u003e3.8 Python Libraries for NLP 131\u003c\/p\u003e \u003cp\u003e3.9 TensorFlow 138\u003c\/p\u003e \u003cp\u003e3.10 Keras 141\u003c\/p\u003e \u003cp\u003e3.11 Pytorch 144\u003c\/p\u003e \u003cp\u003e3.12 Conclusion 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Convolutional Neural Networks 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 153\u003c\/p\u003e \u003cp\u003e4.2 Elements of a Convolutional Neural Network 153\u003c\/p\u003e \u003cp\u003e4.3 Training a CNN 160\u003c\/p\u003e \u003cp\u003e4.4 Extensions of the CNN 166\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Recurrent Neural Networks 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 187\u003c\/p\u003e \u003cp\u003e5.2 RNN Architecture 188\u003c\/p\u003e \u003cp\u003e5.3 Training an RNN 191\u003c\/p\u003e \u003cp\u003e5.4 Long-Term Dependencies: Vanishing and Exploding Gradients 199\u003c\/p\u003e \u003cp\u003e5.5 Deep RNN 201\u003c\/p\u003e \u003cp\u003e5.6 Bidirectional RNN 203\u003c\/p\u003e \u003cp\u003e5.7 Long Short-Term Memory Networks 204\u003c\/p\u003e \u003cp\u003e5.8 Gated Recurrent Units 218\u003c\/p\u003e \u003cp\u003e5.9 Conclusion 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Attention Networks and Transformers 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 225\u003c\/p\u003e \u003cp\u003e6.2 Attention Mechanisms 227\u003c\/p\u003e \u003cp\u003e6.3 Transformers 242\u003c\/p\u003e \u003cp\u003e6.4 BERT 249\u003c\/p\u003e \u003cp\u003e6.5 GPT-2 256\u003c\/p\u003e \u003cp\u003e6.6.1 Comparison between ViTs and CNNs 264\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 269\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Deep Unsupervised Learning I 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 273\u003c\/p\u003e \u003cp\u003e7.2 Restricted Boltzmann Machines 274\u003c\/p\u003e \u003cp\u003e7.3 Deep Belief Networks 278\u003c\/p\u003e \u003cp\u003e7.4 Autoencoders 279\u003c\/p\u003e \u003cp\u003e7.5 Undercomplete Autoencoder 284\u003c\/p\u003e \u003cp\u003e7.6 Sparse Autoencoder 285\u003c\/p\u003e \u003cp\u003e7.7 Denoising Autoencoders 287\u003c\/p\u003e \u003cp\u003e7.8 Convolutional Autoencoder 288\u003c\/p\u003e \u003cp\u003e7.9 Variational Autoencoders 291\u003c\/p\u003e \u003cp\u003e7.10 Conclusion 297\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Deep Unsupervised Learning II 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 301\u003c\/p\u003e \u003cp\u003e8.2 Elements of GAN 303\u003c\/p\u003e \u003cp\u003e8.3 Training a GAN 305\u003c\/p\u003e \u003cp\u003e8.4 Wasserstein GAN 309\u003c\/p\u003e \u003cp\u003e8.5 DCGAN 312\u003c\/p\u003e \u003cp\u003e8.6 cGAN 316\u003c\/p\u003e \u003cp\u003e8.7 CycleGAN 318\u003c\/p\u003e \u003cp\u003e8.8 StyleGAN 323\u003c\/p\u003e \u003cp\u003e8.9 StackGAN 328\u003c\/p\u003e \u003cp\u003e8.10 Diffusion Models 333\u003c\/p\u003e \u003cp\u003e8.11 Conclusion 338\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Deep Bayesian Networks 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 341\u003c\/p\u003e \u003cp\u003e9.2 Bayesian Models 342\u003c\/p\u003e \u003cp\u003e9.3 Bayesian Inference Methods for Deep Learning 344\u003c\/p\u003e \u003cp\u003e9.4 Conclusion 352\u003c\/p\u003e \u003cp\u003eProblems 353\u003c\/p\u003e \u003cp\u003eList of Acronyms 355\u003c\/p\u003e \u003cp\u003eNotation 359\u003c\/p\u003e \u003cp\u003eBibliography 365\u003c\/p\u003e \u003cp\u003eIndex 387\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52501446492440,"sku":"9781119861867","price":63.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119861867.jpg?v=1786239398","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/deep-learning-a-practical-introduction-hardback-9781119861867","provider":"Freshly Printed Books","version":"1.0","type":"link"}