03255nam a2200337 i 4500001001300000008004100013020001500054020001500069020001800084020001800102035002000120041000800140082001100148100002900159245015100188250001600339264006600355300003200421336002600453337002600479338003600505500001300541505116800554520094501722650002102667650002802688856007702716915001202793596000602805949010602811PACKT0000068200212s2018 ob 000 0 eng d a178839576X a1788399900 a9781788395762 a9781788399906 a(Sirsi) a8027690 aeng00a006.311 aMenshawy, Ahmed,eauthor10aDeep learning by example :ba hands-on guide to implementing advanced machine learning algorithms and neural networksh[E-Book] /cAhmed Menshawy. a1st edition 1aBirmingham :bPackt Publishing,c2018e(Packt)fPackt20200417 a442 pages (online resource) atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier aenglisch0 aDeep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Data Science - A Birds' Eye View -- Chapter 2: Data Modeling in Action - The Titanic Example -- Chapter 3: Feature Engineering and Model Complexity - The Titanic Example Revisited -- Chapter 4: Get Up and Running with TensorFlow -- Chapter 5: TensorFlow in Action - Some Basic Examples -- Chapter 6: Deep Feed-forward Neural Networks - Implementing Digit Classification -- Chapter 7: Introduction to Convolutional Neural Networks -- Chapter 8: Object Detection - CIFAR-10 Example -- Chapter 9: Object Detection - Transfer Learning with CNNs -- Chapter 10: Recurrent-Type Neural Networks - Language Modeling -- Chapter 11: Representation Learning - Implementing Word Embeddings -- Chapter 12: Neural Sentiment Analysis -- Chapter 13: Autoencoders - Feature Extraction and Denoising -- Chapter 14: Generative Adversarial Networks -- Chapter 15: Face Generation and Handling Missing Labels -- Appendix: Implementing Fish Recognition -- Other Books You May Enjoy -- Index.3 aDeep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. 0aMachine learning 0aArtificial intelligence40uhttp://portal.igpublish.com/iglibrary/search/PACKT0000068.htmlzVolltext azzwFZJ3 a1 aXX(802769.1)wAUTOc1i802769-1001lELECTRONICmZBrNsYtE-BOOKu17/4/2020xUNKNOWNzUNKNOWN1ONLINE