UID:
kobvindex_INTEBC5675583
Format:
1 online resource (382 pages)
Edition:
2nd ed.
ISBN:
9781788832762
Content:
The book will help you get well-versed with different techniques in Artificial Intelligence such as machine learning, deep learning, natural language processing and more to build smart IoT systems. By the end of the book, you will have practical knowledge on how to implement and manipulate text, audio, and speech data within the IoT system
Note:
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
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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?
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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
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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
Additional Edition:
Print version Kapoor, Amita Hands-On Artificial Intelligence for IoT Birmingham : Packt Publishing, Limited,c2019 ISBN 9781788836067
Language:
English
Keywords:
Electronic books
URL:
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