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Hands-On Artificial Intelligence for IoT
Therearemanyapplicationsthatusedatascienceandanalyticstogaininsightsfromterabytesofdata.Theseapps,however,donotaddressthechallengeofcontinuallydiscoveringpatternsforIoTdata.InHands-OnArtificialIntelligenceforIoT,wecovervariousaspectsofartificialintelligence(AI)anditsimplementationtomakeyourIoTsolutionssmarter.ThisbookstartsbycoveringtheprocessofgatheringandpreprocessingIoTdatagatheredfromdistributedsources.YouwilllearndifferentAItechniquessuchasmachinelearning,deeplearning,reinforcementlearning,andnaturallanguageprocessingtobuildsmartIoTsystems.YouwillalsoleveragethepowerofAItohandlereal-timedatacomingfromwearabledevices.Asyouprogressthroughthebook,techniquesforbuildingmodelsthatworkwithdifferentkindsofdatageneratedandconsumedbyIoTdevicessuchastimeseries,images,andaudiowillbecovered.UsefulcasestudiesonfourmajorapplicationareasofIoTsolutionsareakeyfocalpointofthisbook.Intheconcludingchapters,youwillleveragethepowerofwidelyusedPythonlibraries,TensorFlowandKeras,tobuilddifferentkindsofsmartAImodels.Bytheendofthisbook,youwillbeabletobuildsmartAI-poweredIoTappswithconfidence.
目錄(229章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Hands-On Artificial Intelligence for IoT
- Dedication
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the author
- About the reviewers
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Electrical load forecasting in industry
- STLF using LSTM
- Summary
- 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
- 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
- Leave a review - let other readers know what you think 更新時間：2021-07-02 14:02:40
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