| 청구기호 |
Q325.5 .R36 2019 |
| 형태사항 |
1 online resource (xviii, 302 pages)
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| 언어 |
English |
| 서지주기 |
Includes bibliographical references and index.
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| 내용 |
Cover; Title Page; Copyright; Acknowledgments; About the Author; About the Technical Editor; Credits; Contents; Introduction; How This Book Is Organized; Conventions Used; Who Should Read This Book; Tools You Will Need; Using Python; Using the Frameworks; Setting Up a Notebook; Finding a Dataset; Summary; Chapter 1 Big Data and Artificial Intelligence; Data Is the New Oil and AI Is the New Electricity; Rise of the Machines; Exponential Growth in Processing; A New Breed of Analytics; What Makes AI So Special; Applications of Artificial Intelligence; Building Analytics on Data
Types of Analytics: Based on the ApplicationTypes of Analytics: Based on Decision Logic; Building an Analytics-Driven System; Summary; Chapter 2 Machine Learning; Finding Patterns in Data; The Awesome Machine Learning Community; Types of Machine Learning Techniques; Unsupervised Machine Learning; Supervised Machine Learning; Reinforcement Learning; Solving a Simple Problem; Unsupervised Learning; Supervised Learning: Linear Regression; Gradient Descent Optimization; Applying Gradient Descent to Linear Regression; Supervised Learning: Classification; Analyzing a Bigger Dataset
Metrics for Accuracy: Precision and RecallComparison of Classification Methods; Bias vs. Variance: Underfitting vs. Overfitting; Reinforcement Learning; Model-Based RL; Model-Free RL; Summary; Chapter 3 Handling Unstructured Data; Structured vs. Unstructured Data; Making Sense of Images; Dealing with Videos; Handling Textual Data; Listening to Sound; Summary; Chapter 4 Deep Learning Using Keras; Handling Unstructured Data; Neural Networks; Back-Propagation and Gradient Descent; Batch vs. Stochastic Gradient Descent; Neural Network Architectures; Welcome to TensorFlow and Keras
Bias vs. Variance: Underfitting vs. OverfittingSummary; Chapter 5 Advanced Deep Learning; The Rise of Deep Learning Models; New Kinds of Network Layers; Convolution Layer; Pooling Layer; Dropout Layer; Batch Normalization Layer; Building a Deep Network for Classifying Fashion Images; CNN Architectures and Hyper-Parameters; Making Predictions Using a Pretrained VGG Model; Data Augmentation and Transfer Learning; A Real Classification Problem: Pepsi vs. Coke; Recurrent Neural Networks; Summary; Chapter 6 Cutting-Edge Deep Learning Projects; Neural Style Transfer; Generating Images Using AI
Credit Card Fraud Detection with AutoencodersSummary; Chapter 7 AI in the Modern Software World; A Quick Look at Modern Software Needs; How AI Fits into Modern Software Development; Simple to Fancy Web Applications; The Rise of Cloud Computing; Containers and CaaS; Microservices Architecture with Containers; Kubernetes: A CaaS Solution for Infrastructure Concerns; Summary; Chapter 8 Deploying AI Models as Microservices; Building a Simple Microservice with Docker and Kubernetes; Adding AI Smarts to Your App; Packaging the App as a Container; Pushing a Docker Image to a Repository
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| 주제 |
Artificial intelligence.
Machine learning.
COMPUTERS --General. --bisacsh
Artificial intelligence. --fast --(OCoLC)fst00817247
Big data. --fast --(OCoLC)fst01892965
Data mining. --fast --(OCoLC)fst00887946
Machine learning. --fast --(OCoLC)fst01004795
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| 보유판 및 특별호 저록 |
Print version: Rao, Dattaraj. Keras to Kubernetes : The Journey of a Machine Learning Model to Production. Newark : John Wiley & Sons, Incorporated, ©2019 9781119564836
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| ISBN |
9781119564874, 1119564875, 9781119564843, 1119564840, 9781119564836, 1119564832 |
| QR CODE |
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