Tabla de Contenidos: “…Intro -- Table des matières -- Artificialized land and land take -- Foreword -- 'Land Take', an ambiguous scientific concept -- - Methods of measuring the extent of land take in France -- - The impacts of land take on the characteristics and properties of soils -- - The direct and indirect impacts of land take on the characteristics and functioning of artificialized environments -- - Agricultural land, agricultural activities, and land take -- - Household location strategies and housing construction -- - Determinants of land take by enterprises and transport infrastructure -- - Avoiding or reducing land take, or possibly compensating for its effects -- Bibliography -- List of Authors…”

Materias:

Materias: “…Artificial intelligence Periodicals…”

Materias: “…Artificial intelligence Periodicals…”

Materias: “…Artificial intelligence Periodicals…”

Tabla de Contenidos: “…Front Cover -- Artificial Intelligence For The Internet of Everything -- Copyright -- Contents -- Contributors -- Chapter 1: Introduction -- 1.1. …”

“…As with other technologies introduced in the past decade, artificial intelligence is the subject of many market predictions. …”

Tabla de Contenidos: “…Part I: Neural Network -- 1: The Perceptron Model -- 2: Artificial Neuron -- 3: Neural Networks -- 4: Theory on Learning -- 5: Data Classification -- 6: A Matrix Library -- 7: Matrix-Based Neural Network -- Part II: Genetic Algorithm -- 8: Genetic Algorithm -- 9: Genetic Algorithm in Action -- 10: Traveling Salesman Problem -- 11: Exiting a Maze -- 12: Building Zoomorphic Creatures -- 13: Evolving Zoomorphic Creature -- Part III: Neuroevolution -- 14: Neuroevolution -- 15: Neuroevolution with NEAT -- 16: The MiniMario Video Game -- Last Words…”

Materias: “…Artificial intelligence Data processing…”

Materias: “…Artificial intelligence Moral and ethical aspects…”

Tabla de Contenidos: “…Cover -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Big Data and Artificial Intelligence Systems -- Results pyramid -- What the human brain does best -- Sensory input -- Storage -- Processing power -- Low energy consumption -- What the electronic brain does best -- Speed information storage -- Processing by brute force -- Best of both worlds -- Big Data -- Evolution from dumb to intelligent machines -- Intelligence -- Types of intelligence -- Intelligence tasks classification -- Big data frameworks -- Batch processing -- Real-time processing -- Intelligent applications with Big Data -- Areas of AI -- Frequently asked questions -- Summary -- Chapter 2: Ontology for Big Data -- Human brain and Ontology -- Ontology of information science -- Ontology properties -- Advantages of Ontologies -- Components of Ontologies -- The role Ontology plays in Big Data -- Ontology alignment -- Goals of Ontology in big data -- Challenges with Ontology in Big Data -- RDF-the universal data format -- RDF containers -- RDF classes -- RDF properties -- RDF attributes -- Using OWL, the Web Ontology Language -- SPARQL query language -- Generic structure of an SPARQL query -- Additional SPARQL features -- Building intelligent machines with Ontologies -- Ontology learning -- Ontology learning process -- Frequently asked questions -- Summary -- Chapter 3: Learning from Big Data -- Supervised and unsupervised machine learning -- The Spark programming model -- The Spark MLlib library -- The transformer function -- The estimator algorithm -- Pipeline -- Regression analysis -- Linear regression -- Least square method -- Generalized linear model -- Logistic regression classification technique -- Logistic regression with Spark -- Polynomial regression -- Stepwise regression -- Forward selection -- Backward elimination…”

Tabla de Contenidos: “…; 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 PythonCSV 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 IoTML 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 tricksFeature 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 layerPooling 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…”

Tabla de Contenidos: “…Intro -- Table of Contents -- About the Author -- About the Technical Reviewers -- Acknowledgments -- Introduction -- Part I: Getting Started with Neural Networks -- Chapter 1: Learning About Neural Networks -- Biological and Artificial Neurons -- Activation Functions -- Summary -- Chapter 2: Internal Mechanics of Neural Network Processing -- Function to Be Approximated -- Network Architecture -- Forward Pass Calculation -- Input Record 1 -- Input Record 2 -- Input Record 3 -- Input Record 4 -- Back-Propagation Pass -- Function Derivative and Function Divergent -- Most Commonly Used Function Derivatives -- Summary -- Chapter 3: Manual Neural Network Processing -- Example: Manual Approximation of a Function at a Single Point -- Building the Neural Network -- Forward Pass Calculation -- Hidden Layers -- Output Layer -- Backward Pass Calculation -- Calculating Weight Adjustments for the Output-Layer Neurons -- Calculating Adjustment for W211 -- Calculating Adjustment for W212 -- Calculating Adjustment for W213 -- Calculating Weight Adjustments for Hidden-Layer Neurons -- Calculating Adjustment for W111 -- Calculating Adjustment for W112 -- Calculating Adjustment for W121 -- Calculating Adjustment for W122 -- Calculating Adjustment for W131 -- Calculating Adjustment for W132 -- Updating Network Biases -- Back to the Forward Pass -- Hidden Layers -- Output Layer -- Matrix Form of Network Calculation -- Digging Deeper -- Mini-Batches and Stochastic Gradient -- Summary -- Part II: Neural Network Java Development Environment -- Chapter 4: Configuring Your Development Environment -- Installing the Java Environment and NetBeans on Your Windows Machine -- Installing the Encog Java Framework -- Installing the XChart Package -- Summary -- Chapter 5: Neural Networks Development Using the Java Encog Framework…”

Materias: “…Artificial intelligence Medical applications Periodicals…”

Materias:

Materias: “…Artificial intelligence…”

Materias: “…Artificial cells Periodicals…”

Materias: “…Artificial intelligence Economic aspects…”