LEADER 06761nam 2200457 450 001 9910795318203321 005 20200520144314.0 035 $a(CKB)4970000000061404 035 $a(MiAaPQ)EBC5675583 035 $a(CaSebORM)9781788836067 035 $a(Au-PeEL)EBL5675583 035 $a(OCoLC)1086025719 035 $a(EXLCZ)994970000000061404 100 $a20190220d2019 uy| 0 101 0 $aeng 135 $aurcn| ||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHands-on artificial intelligence for IoT $eexpert machine learning and deep learning techniques for developing smarter IoT systems /$fAmita Kapoor 205 $a1st edition 210 1$aBirmingham :$cPackt,$d2019. 215 $a1 online resource (390 pages) 300 $aIncludes index. 311 $a1-78883-606-5 327 $aCover; 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 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 330 $aBuild smarter systems by combining artificial intelligence and the Internet of Things - two of the most talked about topics today Key Features Leverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT data Process IoT data and predict outcomes in real time to build smart IoT models Cover practical case studies on industrial IoT, smart cities, and home automation Book Description There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence. What you will learn Apply different AI techniques including machine learning and deep learning using TensorFlow and Keras Access and process data from various distributed sources Perform supervised and unsupervised machine learning for IoT data Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms Forecast time-series data using deep learning methods Implementing AI from case studies in Personal IoT, Industrial IoT, and Smart Cities Gain unique insights from data obtained from wearable devices and smart devices Who this book is for If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how po... 606 $aArtificial intelligence 606 $aInternet of things 606 $aMachine learning 615 0$aArtificial intelligence. 615 0$aInternet of things. 615 0$aMachine learning. 700 $aKapoor$b Amita$01209903 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910795318203321 996 $aHands-on artificial intelligence for IoT$93765059 997 $aUNINA LEADER 01259nam0 22002891i 450 001 UON00083325 005 20231205102436.523 010 $a92-310-1740-3 100 $a20020107d1980 |0itac50 ba 101 $aeng 102 $aFR 105 $a|||| ||||| 200 1 $aHistorical relations across the Indian Ocean$ereport and papers of the meeting of experts organized by Unesco at Port Luis, Mauritius, from 15 to 19 july 1974 210 $a[Paris]$cUnesco$dc1980 215 $a192 p.$d24 cm 316 $a*Dati inesatti$5IT-UONSI AORST/108 410 1$1001UON00066271$12001 $aˆThe ‰general history of Africa$estudies and documents$v3 606 $aAFRICA ORIENTALE$xRelazioni estere$3UONC025328$2FI 620 $aFR$dParis$3UONL002984 676 $a327.676$cRELAZIONI ESTERE DELL'AFRICA ORIENTALE$v21 712 $aUnesco Publishing$3UONV247821$4650 801 $aIT$bSOL$c20240220$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00083325 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI AOR ST 108 $eSI AA 5652 5 108 *Dati inesatti 996 $aHistorical relations across the Indian Ocean$91296099 997 $aUNIOR LEADER 02880nam 2200421 450 001 9910820016403321 005 20210329145751.0 010 $a3-8325-9205-9 035 $a(CKB)4340000000248753 035 $a(MiAaPQ)EBC5313470 035 $a5c7aad7e-8dd4-4170-8f05-7583b0dd2d03 035 $a(EXLCZ)994340000000248753 100 $a20180521d2017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aConfined magnon modes and anisotropic exchange interaction in ultrathin Co films /$fvorgelegt von Ying-Jiun Chen 210 1$aBerlin :$cLogos Verlag,$d[2017] 210 4$d©2017 215 $a1 online resource (128 pages) 300 $aPublicationDate: 20170418 311 $a3-8325-4470-4 330 $aLong description: A fundamental need in wireless communication and modern computer processors is to fabricate faster, smaller, and lower power-consumption circuits. A promising approach is the utilization of spin waves, or rather, magnons in ferromagnetic films. Quantum confinement in ultra-thin films permits the coexistence of several exchange-dominated magnon modes with terahertz-range frequencies and sub-nanometer length scales. By means of spin-polarized electron energy loss spectroscopy (SPEELS), these exchange-dominated terahertz magnons are directly probed in ultra-thin cobalt films on Ir(001), Cu(001) and Pt(111) single crystal surfaces. The dispersion relation of the quantized magnon modes depends particularly on the interatomic exchange interaction in individual layers. By tuning the exchange interaction, modes with opposite group velocities, and thus an opposite propagation direction of the wave packets, can be generated. Ab initio theoretical calculations as well as an analytical Heisenberg model reveal a spatial localization of the modes at the surface, interior, and interface of the film that opens an experimental access to the layer-dependent magnetic properties. In itinerant ferromagnets like cobalt the magnetic properties depend sensitively on many-body correlation effects in the electronic structure. Here, it is shown for the first time that spin-dependent correlations lead to a pronounced renormalization of the energy of the highest magnon mode by up to 260 meV, explaining the significant overestimation of theoretically predicted magnon energies and interatomic exchange interaction found in literature. 606 $aMagnons 606 $aSpin waves 615 0$aMagnons. 615 0$aSpin waves. 676 $a530.41 700 $aChen$b Ying-Jiun$01672816 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910820016403321 996 $aConfined magnon modes and anisotropic exchange interaction in ultrathin Co films$94036414 997 $aUNINA