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Kapoor, Amita. HANDS-ON ARTIFICIAL INTELLIGENCE FOR IOT [[electronic resource]]: expert techniques for developing smarter iot systems ... through machine learning and deep learning with py. — [Place of publication not identified]: PACKT Publishing Limited, 2019. — 1 online resource — <URL:http://elib.fa.ru/ebsco/2018973.pdf>.

Дата создания записи: 18.02.2019

Тематика: Artificial intelligence.; Machine learning.; Internet of things.; Artificial intelligence.; Internet of things.; Machine learning.; COMPUTERS / General

Коллекции: EBSCO

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Оглавление

  • Cover
  • Title Page
  • Copyright and Credits
  • Dedication
  • About Packt
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Principles and Foundations of IoT and AI
    • What is IoT 101?
      • IoT reference model
      • IoT platforms
      • IoT verticals
    • Big data and IoT
    • Infusion of AI – data science in IoT
      • Cross-industry standard process for data mining
      • AI platforms and IoT platforms
    • Tools used in this book
      • TensorFlow
      • Keras
      • Datasets
        • The combined cycle power plant dataset
        • Wine quality dataset
        • Air quality data
    • Summary
  • Chapter 2: Data Access and Distributed Processing for IoT
    • TXT format
      • Using TXT files in Python
    • CSV format
      • Working with CSV files with the csv module
      • Working with CSV files with the pandas module
      • Working with CSV files with the NumPy module
    • XLSX format
      • Using OpenPyXl for XLSX files
      • Using pandas with XLSX files
    • Working with the JSON format
      • Using JSON files with the JSON module
      • JSON files with the pandas module
    • HDF5 format
      • Using HDF5 with PyTables
      • Using HDF5 with pandas
      • Using HDF5 with h5py
    • SQL data
      • The SQLite database engine
      • The MySQL database engine
    • NoSQL data
    • HDFS
      • Using hdfs3 with HDFS
      • Using PyArrow's filesystem interface for HDFS
    • Summary
  • Chapter 3: Machine Learning for IoT
    • ML and IoT
    • Learning paradigms
    • Prediction using linear regression
      • Electrical power output prediction using regression
    • Logistic regression for classification
      • Cross-entropy loss function
      • Classifying wine using logistic regressor
    • Classification using support vector machines
      • Maximum margin hyperplane
      • Kernel trick
      • Classifying wine using SVM
    • Naive Bayes
      • Gaussian Naive Bayes for wine quality
    • Decision trees
      • Decision trees in scikit
      • Decision trees in action
    • Ensemble learning
      • Voting classifier
      • Bagging and pasting
    • Improving your model – tips and tricks
      • Feature scaling to resolve uneven data scale
      • Overfitting
        • Regularization
        • Cross-validation
      • No Free Lunch theorem
      • Hyperparameter tuning and grid search
    • Summary
  • Chapter 4: Deep Learning for IoT
    • Deep learning 101
      • Deep learning—why now?
      • Artificial neuron
      • Modelling single neuron in TensorFlow
    • Multilayered perceptrons for regression and classification
      • The backpropagation algorithm
      • Energy output prediction using MLPs in TensorFlow
      • Wine quality classification using MLPs in TensorFlow
    • Convolutional neural networks
      • Different layers of CNN
        • The convolution layer
        • Pooling layer
      • Some popular CNN model
      • LeNet to recognize handwritten digits
    • Recurrent neural networks
      • Long short-term memory
      • Gated recurrent unit
    • Autoencoders
      • Denoising autoencoders
      • Variational autoencoders
    • Summary
  • Chapter 5: Genetic Algorithms for IoT
    • Optimization
      • Deterministic and analytic methods
        • Gradient descent method
        • Newton-Raphson method
      • Natural optimization methods
        • Simulated annealing
        • Particle Swarm Optimization
        • Genetic algorithms
    • Introduction to genetic algorithms
      • The genetic algorithm
        • Crossover
        • Mutation
      • Pros and cons
        • Advantages
        • Disadvantages
    • Coding genetic algorithms using Distributed Evolutionary Algorithms in Python
      • Guess the word
      • Genetic algorithm for CNN architecture
      • Genetic algorithm for LSTM optimization
    • Summary
  • Chapter 6: Reinforcement Learning for IoT
    • Introduction
      • RL terminology
        • Deep reinforcement learning
      • Some successful applications
    • Simulated environments
      • OpenAI gym
    • Q-learning
      • Taxi drop-off using Q-tables
    • Q-Network
      • Taxi drop-off using Q-Network
      • DQN to play an Atari game
      • Double DQN
      • Dueling DQN
    • Policy gradients
      • Why policy gradients?
      • Pong using policy gradients
      • The actor-critic algorithm
    • Summary
  • Chapter 7: Generative Models for IoT
    • Introduction
    • Generating images using VAEs
      • VAEs in TensorFlow
    • GANs
      • Implementing a vanilla GAN in TensorFlow
      • Deep Convolutional GANs 
      • Variants of GAN and its cool applications
        • Cycle GAN
        • Applications of GANs
    • Summary
  • Chapter 8: Distributed AI for IoT
    • Introduction
      • Spark components
    • Apache MLlib
      • Regression in MLlib
      • Classification in MLlib
      • Transfer learning using SparkDL
    • Introducing H2O.ai
      • H2O AutoML
      • Regression in H2O
      • Classification in H20
    • Summary
  • Chapter 9: Personal and Home IoT
    • Personal IoT
      • SuperShoes by MIT
      • Continuous glucose monitoring
        • Hypoglycemia prediction using CGM data
      • Heart monitor
      • Digital assistants
    • IoT and smart homes
      • Human activity recognition
        • HAR using wearable sensors
        • HAR from videos
      • Smart lighting
      • Home surveillance
    • Summary
  • Chapter 10: AI for the Industrial IoT
    • Introduction to AI-powered industrial IoT
      • Some interesting use cases
    • Predictive maintenance using AI
      • Predictive maintenance using Long Short-Term Memory
      • Predictive maintenance advantages and disadvantages
    • Electrical load forecasting in industry
      • STLF using LSTM
    • Summary
  • Chapter 11: AI for Smart Cities IoT
    • Why do we need smart cities?
    • Components of a smart city
      • Smart traffic management
      • Smart parking
      • Smart waste management
      • Smart policing
      • Smart lighting
      • Smart governance
    • Adapting IoT for smart cities and the necessary steps
      • Cities with open data
        • Atlanta city Metropolitan Atlanta Rapid Transit Authority data
        • Chicago Array of Things data
      • Detecting crime using San Francisco crime data
    • Challenges and benefits
    • Summary
  • Chapter 12: Combining It All Together
    • Processing different types of data
      • Time series modeling
      • Preprocessing textual data
      • Data augmentation for images
      • Handling videos files
      • Audio files as input data
    • Computing in the cloud
      • AWS
      • Google Cloud Platform
      • Microsoft Azure
    • Summary
  • Other Books You May Enjoy
  • Index

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