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001437881 001__ 1437881
001437881 003__ OCoLC
001437881 005__ 20230309004237.0
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001437881 019__ $$a1258219370$$a1290683601
001437881 020__ $$a9783030686246$$q(electronic book)
001437881 020__ $$a3030686248$$q(electronic book)
001437881 020__ $$z9783030686239
001437881 020__ $$z303068623X
001437881 0247_ $$a10.1007/978-3-030-68624-6$$2doi
001437881 035__ $$aSP(OCoLC)1259321433
001437881 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dEBLCP$$dNOC$$dYDX$$dGW5XE$$dYDXIT$$dOCLCO$$dGZM$$dOCLCF$$dUKAHL$$dN$T$$dYDX$$dOCLCQ$$dOCLCO$$dOCLCQ
001437881 049__ $$aISEA
001437881 050_4 $$aQ335$$b.D83 2021
001437881 08204 $$a006.3$$223
001437881 1001_ $$aDube, Simant,$$eauthor.
001437881 24513 $$aAn intuitive exploration of artificial intelligence :$$btheory and applications of deep learning /$$cSimant Dube.
001437881 264_1 $$aCham :$$bSpringer International Publishing AG,$$c2021.
001437881 300__ $$a1 online resource (355 pages)
001437881 336__ $$atext$$btxt$$2rdacontent
001437881 337__ $$acomputer$$bc$$2rdamedia
001437881 338__ $$aonline resource$$bcr$$2rdacarrier
001437881 504__ $$aIncludes bibliographical references and index.
001437881 5050_ $$aIntro -- Preface -- Contents -- Acronyms -- Author's Note -- Part I Foundations -- 1 AI Sculpture -- 1.1 Manifolds in High Dimensions -- 1.2 Sculpting Process -- 1.3 Notational Convention -- 1.4 Regression and Classification -- 1.4.1 Linear Regression and Logistic Regression -- 1.4.2 Regression Loss and Cross-Entropy Loss -- 1.4.3 Sculpting with Shades -- 1.5 Discriminative and Generative AI -- 1.6 Success of Discriminative Methods -- 1.7 Feature Engineering in Classical ML -- 1.8 Supervised and Unsupervised AI -- 1.9 Beyond Manifolds -- 1.10 Chapter Summary -- 2 Make Me Learn
001437881 5058_ $$a2.1 Learnable Parameters -- 2.1.1 The Power of a Single Neuron -- 2.1.2 Neurons Working Together -- 2.2 Backpropagation of Gradients -- 2.2.1 Partial Derivatives -- 2.2.2 Forward and Backward Passes -- 2.3 Stochastic Gradient Descent -- 2.3.1 Handling Difficult Landscapes -- 2.3.2 Stabilization of Training -- 2.4 Chapter Summary -- 3 Images and Sequences -- 3.1 Convolutional Neural Networks -- 3.1.1 The Biology of the Visual Cortex -- 3.1.2 Pattern Matching -- 3.1.3 3-D Convolution -- 3.2 Recurrent Neural Networks -- 3.2.1 Neurons with States -- 3.2.2 The Power of Recurrence
001437881 5058_ $$a3.2.3 Going Both Ways -- 3.2.4 Attention -- 3.3 Self-Attention -- 3.4 LSTM -- 3.5 Beyond Images and Sequences -- 3.6 Chapter Summary -- 4 Why AI Works -- 4.1 Convex Polytopes -- 4.2 Piecewise Linear Function -- 4.2.1 Subdivision of the Input Space -- 4.2.2 Piecewise Non-linear Function -- 4.2.3 Carving Out the Feature Spaces -- 4.3 Expressive Power of AI -- 4.4 Convolutional Neural Network -- 4.5 Recurrent Neural Network -- 4.6 Architectural Variations -- 4.7 Attention and Carving -- 4.8 Optimization Landscape -- 4.8.1 Graph-Induced Polynomial -- 4.8.2 Gradient of the Loss Function
001437881 5058_ $$a4.8.3 Visualization -- 4.8.4 Critical Points -- 4.9 The Mathematics of Loss Landscapes -- 4.9.1 Random Polynomial Perspective -- 4.9.2 Random Matrix Perspective -- 4.9.3 Spin Glass Perspective -- 4.9.4 Computational Complexity Perspective -- 4.9.5 SGD and Critical Points -- 4.9.6 Confluence of Perspectives -- 4.10 Distributed Representation and Intrinsic Dimension -- 4.11 Chapter Summary -- 5 Novice to Maestro -- 5.1 How AI Learns to Sculpt -- 5.1.1 Training Data -- 5.1.2 Evaluation Metrics -- 5.1.3 Hyperparameter Search -- 5.1.4 Regularization -- 5.1.5 Bias and Variance
001437881 5058_ $$a5.1.6 A Fairy Tale in the Land of ML -- 5.2 Learning Curves -- 5.3 From the Lab to the Dirty Field -- 5.4 System Design -- 5.5 Flavors of Learning -- 5.6 Ingenuity and Big Data in the Success of AI -- 5.7 Chapter Summary -- 6 Unleashing the Power of Generation -- 6.1 Creating Universes -- 6.2 To Recognize It, Learn to Draw It -- 6.3 General Definition -- 6.4 Generative Parameters -- 6.5 Generative AI Models -- 6.5.1 Restricted Boltzmann Machines -- 6.5.2 Autoencoders -- 6.5.3 Variational Autoencoder -- 6.5.4 Pixel Recursive Models -- 6.5.5 Generative Adversarial Networks
001437881 506__ $$aAccess limited to authorized users.
001437881 520__ $$aThis book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future. An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential.
001437881 650_0 $$aArtificial intelligence.
001437881 650_6 $$aIntelligence artificielle.
001437881 655_0 $$aElectronic books.
001437881 77608 $$iPrint version:$$aDube, Simant.$$tAn Intuitive Exploration of Artificial Intelligence.$$dCham : Springer International Publishing AG, ©2021$$z9783030686239
001437881 852__ $$bebk
001437881 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-68624-6$$zOnline Access$$91397441.1
001437881 909CO $$ooai:library.usi.edu:1437881$$pGLOBAL_SET
001437881 980__ $$aBIB
001437881 980__ $$aEBOOK
001437881 982__ $$aEbook
001437881 983__ $$aOnline
001437881 994__ $$a92$$bISE