Tabla de Contenidos: “…Bar to Interference by Courts in Electoral Matters -- Amendment of Article 280 -- Addition of Twelfth Schedule -- Twelfth Schedule -- (Article 243-W) -- Extract of Article 243-I and 243-K from Clause 2 of the Constitution (73rd Amendment) Act, 1992 Which Are Referred to in the Constitution (74th Amendment) Act, 1992 -- 243-I. …”

Tabla de Contenidos: “…5.2.6 Company Activity -- 5.2.7 Future Projections -- References -- Further reading -- 6 Nanotechnology for Health, Food, and Hygiene -- 6.1 In Vivo Nanoparticles -- 6.2 In Vivo Devices -- 6.3 In Vivo Nanostructured Materials -- 6.4 In Vitro Nanostructured Materials -- 6.5 Labs-on-Chips -- 6.6 Information Technology -- 6.7 Paramedicine -- 6.8 Food -- 6.8.1 Packaging -- 6.8.2 Sensors -- 6.8.3 Nano-Additives -- 6.8.4 Consumer Choice -- 6.9 Hygiene -- 6.10 Expected Market Size -- References -- Further Reading -- 7 Nanotechnology for Energy -- 7.1 Energy Harvesting -- 7.2 Production and Storage -- 7.2.1 Energy Production -- 7.2.2 Energy Storage -- Electrical Storage Devices -- Hydrogen Storage -- 7.3 Energy Ef ciency -- 7.3.1 Lighting -- 7.3.2 Computation -- 7.3.3 Electrical Cabling -- 7.4 Localized Manufacture -- References -- Further Reading -- 8 Information Technologies -- 8.1 Silicon Microelectronics -- 8.2 Flexible Electronics -- 8.3 Heat Management -- 8.4 Data Storage Technologies -- 8.5 Display Technologies -- 8.6 Molecule or Particle Sensing Technologies -- 8.7 The Internet of Things -- References -- Further Reading -- 9 Miscellaneous Applications -- 9.1 Aerospace and Automotive Industries -- 9.2 Agriculture -- 9.3 Architecture and Construction -- 9.4 Catalysis -- 9.5 Environment and Air Quality -- 9.6 Lubricants -- 9.7 Minerals and Metal Extraction -- 9.8 Paper -- 9.9 Security and Military -- 9.10 Textiles -- 9.11 Transport -- References -- Further Reading -- 10 The Design of Nanotechnology Products -- 10.1 The Challenge of Vasti cation -- 10.2 Enhancing Traditional Design Routes -- 10.3 Crowdsourcing -- 10.4 Materials Selection -- 10.5 Formulation -- 10.6 Quality Control -- 10.7 Biomimicry -- 10.8 Nanodevices Moving in Viscous Media -- References -- Further Reading -- Part 3 Organizing Nanotechnology Business…”

Tabla de Contenidos: “…. -- Chapter 8: Application of Deep Learning in Text Mining -- Performing preprocessing of textual data and extraction of sentiments -- How to do it... -- How it works... -- Analyzing documents using tf-idf -- How to do it... -- How it works... -- Performing sentiment prediction using LSTM network -- How to do it... -- How it works... -- Application using text2vec examples -- How to do it... -- How it works... -- Chapter 9: Application of Deep Learning to Signal processing -- Introducing and preprocessing music MIDI files -- Getting ready -- How to do it... -- Building an RBM model -- Getting ready -- How to do it... -- Generating new music notes -- How to do it... -- Chapter 10: Transfer Learning -- Introduction…”

Tabla de Contenidos: “…-- 8.1.1 The five stages of the Datashader rendering pipeline -- 8.1.2 Creating a Datashader Visualization -- 8.2 Plotting location data as an interactive heatmap -- 8.2.1 Preparing geographic data for map tiling -- 8.2.2 Creating the interactive heatmap -- Summary -- Part 3: Extending and deploying Dask -- Chapter 9: Working with Bags and Arrays -- 9.1 Reading and parsing unstructured data with Bags -- 9.1.1 Selecting and viewing data from a Bag -- 9.1.2 Common parsing issues and how to overcome them -- 9.1.3 Working with delimiters -- 9.2 Transforming, filtering, and folding elements -- 9.2.1 Transforming elements with the map method -- 9.2.2 Filtering Bags with the filter method -- 9.2.3 Calculating descriptive statistics on Bags -- 9.2.4 Creating aggregate functions using the foldby method -- 9.3 Building Arrays and DataFrames from Bags -- 9.4 Using Bags for parallel text analysis with NLTK -- 9.4.1 The basics of bigram analysis -- 9.4.2 Extracting tokens and filtering stopwords -- 9.4.3 Analyzing the bigrams -- Summary -- Chapter 10: Machine learning with Dask-ML -- 10.1 Building linear models with Dask-ML -- 10.1.1 Preparing the data with binary vectorization -- 10.1.2 Building a logistic regression model with Dask-ML -- 10.2 Evaluating and tuning Dask-ML models -- 10.2.1 Evaluating Dask-ML models with the score method -- 10.2.2 Building a naïve Bayes classifier with Dask-ML -- 10.2.3 Automatically tuning hyperparameters -- 10.3 Persisting Dask-ML models -- Summary -- Chapter 11: Scaling and deploying Dask -- 11.1 Building a Dask cluster on Amazon AWS with Docker -- 11.1.1 Getting started…”

Tabla de Contenidos: “…Solano and Carlos David Grande-Tovar -- Nanocomposites of Chitosan/Graphene Oxide/Titanium Dioxide Nanoparticles/Blackberry -- Waste Extract as Potential Bone Substitutes -- Reprinted from: Polymers 2021, 13, 3877, doi:10.3390/polym13223877 195 -- Cheng-Wei Huang, Ya-Ying Chang, Chih-Chia Cheng, Meng-Ting Hung and Mohamed -- Gamal Mohamed -- Self-Assembled Supramolecular Micelles Based on Multiple Hydrogen Bonding Motifs for the -- Encapsulation and Release of Fullerene -- Reprinted from: Polymers 2022, 14, 4923, doi:10.3390/polym14224923 209 -- Wei-Cheng Cheng, Yi-Ting Hsieh and Wei-Ren Liu -- Enhanced Thermal Conductivity of Silicone Composites Filled with Few-Layered -- Hexagonal Boron Nitride -- Reprinted from: Polymers 2020, 12, 2072, doi:10.3390/polym12092072 223 -- Ahmed Elmahdy, Aldobenedetto Zotti, Simona Zuppolini, Mauro Zarrelli, Anna Borriello -- and Patricia Verleysen -- Effect of Strain Rate and Silica Filler Content on the Compressive Behavior of RTM6 -- Epoxy-Based Nanocomposites -- Reprinted from: Polymers 2021, 13, 3735, doi:10.3390/polym13213735 237 -- Yu-Hong Yeh, Kuei-Ting Hsu, Chia-Hung Huang and Wei-Ren Liu -- Facile and Green Process to Synthesize a Three-Dimensional Network Few-Layer -- Graphene/Carbon Nanotube Composite for Electromagnetic Interference Shielding -- Reprinted from: Polymers 2022, 14, 1892, doi:10.3390/polym14091892 257 -- Sveta Zhiraslanovna Ozkan, Aleksandr Ivanovich Kostev, Petr Aleksandrovich Chernavskii -- and Galina Petrovna Karpacheva -- Novel Hybrid Nanomaterials Based on Poly-N-Phenylanthranilic Acid and Magnetic -- Nanoparticles with Enhanced Saturation Magnetization -- Reprinted from: Polymers 2022, 14, 2935, doi:10.3390/polym14142935 269 -- Arkarapol Thumwong, Manchusa Chinnawet, Preawpraw Intarasena, Chanis Rattanapongs, -- Shinji Tokonami and Tetsuo Ishikawa et al. -- A Comparative Study on X-ray Shielding and Mechanical Properties of Natural Rubber Latex -- Nanocomposites Containing Bi2O3 or BaSO4: Experimental and Numerical Determination -- Reprinted from: Polymers 2022, 14, 3654, doi:10.3390/polym14173654 289 -- Oscar Ram ́ırez, Mat ́ıas Leal, Ximena Briones, Marcela Urz ́ua, Sebasti ́an Bonardd and Cesar -- Sald ́ıas et al. -- New Hybrid Nanocomposites with Catalytic Properties Obtained by In Situ Preparation of Gold -- Nanoparticles on Poly (Ionic Liquid)/Poly (4-Vinylpyridine) Nanofibers -- Reprinted from: Polymers 2022, 14, 3782, doi:10.3390/polym14183782 305 -- Pablo Ramos, Tamara Calvo-Correas, Arantxa Eceiza and Javier Gonz ́alez-Benito -- Nonwoven Mats Based on Segmented Biopolyurethanes Filled with MWCNT Prepared by -- Solution Blow Spinning -- Reprinted from: Polymers 2022, 14, 4175, doi:10.3390/polym14194175 321 -- Se Jung Lee, Seo Jeong Yoon and In-Yup Jeon -- Graphene/Polymer Nanocomposites: Preparation, Mechanical Properties, and Application -- Reprinted from: Polymers 2022, 14, 4733, doi:10.3390/polym14214733 337…”

Tabla de Contenidos: “…Eng. 2014, 2(1), 66-80. http://www.mdpi.com/2077-1312/2/1/66 -- Taiping Wang, Tarang Khangaonkar, Wen Long and Gary Gill -- Development of a Kelp-Type Structure Module in a Coastal Ocean Model to Assess the Hydrodynamic Impact of Seawater Uranium Extraction Technology Reprinted from: J. Mar. Sci. Eng. 2014, 2(1), 81-92. http://www.mdpi.com/2077-1312/2/1/81 -- Parmeshwar L. …”

Tabla de Contenidos: “…Garthwaite Deformation Monitoring in On the Design of Multi-Frequency InSAR Radar Corner Reflectors for Reprinted from: Remote Sens. 2017, 9, 648, doi: 10.3390/rs9070648 -- Andrea Monti-Guarnieri, Davide Giudici and Andrea Recchia Identification of C-Band Radio Frequency Interferences from Sentinel-1 Data Reprinted from: Remote Sens. 2017, 9, 1183, doi: 10.3390/rs9111183 -- Heng Zhang, Jiangwen Tang, Robert Wang, Yunkai Deng, Wei Wang and Ning Li An Accelerated Backprojection Algorithm for Monostatic and Bistatic SAR Processing Reprinted from: Remote Sens. 2018, 10, 140, doi: 10.3390/rs10010140 -- Seok Kim, Jiwoong Yu, Se-Yeon Jeon, Aulia Dewantari and Min-Ho Ka Signal Processing for a Multiple-Input, Multiple-Output (MIMO) Video Synthetic Aperture Radar (SAR) with Beat Frequency Division Frequency-Modulated Continuous Wave (FMCW) Reprinted from: Remote Sens. 2017, 9, 491, doi: 10.3390/rs9050491 -- Jeong-Won Park, Jae Hun Kim and Joong-Sun Won Fast and Efficient Correction of Ground Moving Targets in a Synthetic Aperture Radar, Single-Look Complex Image Reprinted from: Remote Sens. 2017, 9, 926, doi: 10.3390/rs9090926 -- Yuncheng Bu, Xingdong Liang, Yu Wang, Fubo Zhang and Yanlei Li A Unified Algorithm for Channel Imbalance and Antenna Phase Center Position Calibration of a Single-Pass Multi-Baseline TomoSAR System Reprinted from: Remote Sens. 2018, 10, 456, doi: 10.3390/rs10030456 -- Markus Even and Karsten Schulz InSAR Deformation Analysis with Distributed Scatterers: A Review Complemented by New Advances Reprinted from: Remote Sens. 2018, 10, 744, doi: 10.3390/rs10050744 -- Xin Tian, Rakesh Malhotra, Bing Xu, Haoping Qi and Yuxiao Ma Modeling Orbital Error in InSAR Interferogram Using Frequency and Spatial Domain Based Methods Reprinted from: Remote Sens. 2018, 10, 508, doi: 10.3390/rs10040508 -- Cheng Wang, Liang Chen, Haisheng Zhao, Zheng Lu, Mingming Bian, Running Zhang and Jian Feng Ionospheric Reconstructions Using Faraday Rotation in Spaceborne Polarimetric SAR Data Reprinted from: Remote Sens. 2017, 9, 1169, doi: 10.3390/rs9111169 -- Yuting Dong, Houjun Jiang, Lu Zhang and Mingsheng Liao An Efficient Maximum Likelihood Estimation Approach of Multi-Baseline SAR Interferometry for Refined Topographic Mapping in Mountainous Areas Reprinted from: Remote Sens. 2018, 10, 454, doi: 10.3390/rs10030454 -- Jingwen Zhao, Jicang Wu, Xiaoli Ding and Mingzhou Wang Elevation Extraction and Deformation Monitoring by Multitemporal InSAR of Lupu Bridge in Shanghai Reprinted from: Remote Sens. 2017, 9, 897, doi: 10.3390/rs9090897 -- Julia Neelmeijer, Tilo Schöne, Robert Dill, Volker Klemann and Mahdi Motagh Ground Deformations around the Toktogul Reservoir, Kyrgyzstan, from Envisat ASAR and Sentinel-1 Data-A Case Study about the Impact of Atmospheric Corrections on InSAR Time Series Reprinted from: Remote Sens. 2018, 10, 462, doi: 10.3390/rs10030462 -- Luyi Sun, Jan-Peter Muller and Jinsong Chen Time Series Analysis of Very Slow Landslides in the Three Gorges Region through Small Baseline SAR Offset Tracking Reprinted from: Remote Sens. 2017, 9, 1314, doi: 10.3390/rs9121314 -- Xuguo Shi, Houjun Jiang, Lu Zhang and Mingsheng Liao Landslide Displacement Monitoring with Split- Bandwidth Interferometry: A Case Study of the Shuping Landslide in the Three Gorges Area Reprinted from: Remote Sens. 2017, 9, 937, doi: 10.3390/rs9090937 -- Ludivine Libert, Dominique Derauw, Nicolas d'Oreye, Christian Barbier and Anne Orban Split-Band Interferometry-Assisted Phase Unwrapping for the Phase Ambiguities Correction Reprinted from: Remote Sens. 2017, 9, 879, doi: 10.3390/rs9090879 -- Ali Ghafouri, Jalal Amini, Mojtaba Dehmollaian and Mohammad Ali Kavoosi Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data Reprinted from: Remote Sens. 2017, 9, 445, doi: 10.3390/rs9050445 -- Gerardo Di Martino, Antonio Iodice, Daniele Riccio, Giuseppe Ruello and Ivana Zinno The Role of Resolution in the Estimation of Fractal Dimension Maps From SAR Data Reprinted from: Remote Sens. 2018, 10, 9, doi: 10.3390/rs10010009 -- Xinping Deng, Carlos López-Martínez, Jinsong Cheng and Pengpeng Han Statistical Modeling of Polarimetric SAR Data: A Survey and Challenges Reprinted from: Remote Sens. 2017, 9, 348, doi: 10.3390/rs9040348 -- Qihao Chen, Linlin Li, Qiao Xu, Shuai Yang, Xuguo Shi and Xiuguo Liu Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach Reprinted from: Remote Sens. 2017, 9, 570, doi: 10.3390/rs9060570 -- Chensong Tao, Siwei Chen, Yongzhen Li and Shunping Xiao PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain Reprinted from: Remote Sens. 2017, 9, 660, doi: 10.3390/rs9070660 -- Andreas Braun and Volker Hochschild A SAR-Based Index for Landscape Changes in African Savannas Reprinted from: Remote Sens. 2017, 9, 359, doi: 10.3390/rs9040359 -- Amir Behnamian, Sarah Banks, Koreen Milard, Lori White, Jon Pasher, Zhaohua Chen, Jason Duff, Laura Bourgeau-Chavez and Michael Battaglia Semi-Automated Surface Water Detection with Synthetic Aperture Radar Data: A Wetland Case Study Reprinted from: Remote Sens. 2017, 9, 1209, doi: 10.3390/rs9121209 -- Prosper Washaya, Timo Balz and Bahaa Mohamadi Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas Reprinted from: Remote Sens. 2018, 10, 1026, doi: 10.3390/rs10071026 -- Aobo Zhai, Xianbin Wen, Haixia Xu, Liming Yuan and Qingxia Meng Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images Reprinted from: Remote Sens. 2017, 9, 1085, doi: 10.3390/rs9101085 -- Yusuf Eshqi Molan, Jin-Woo Kim, Zhong Lu and Piyush Agram L-Band Temporal Coherence Assessment and Modeling Using Amplitude and Snow Depth over Interior Alaska Reprinted from: Remote Sens. 2018, 10, 150, doi: 10.3390/rs10010150…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Dedication -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: Debugging for Machine Learning Modeling -- Chapter 1: Beyond Code Debugging -- Technical requirements -- Machine learning at a glance -- Types of machine learning modeling -- Supervised learning -- Unsupervised learning -- Self-supervised learning -- Semi-supervised learning -- Reinforcement learning -- Generative machine learning -- Debugging in software development -- Error messages in Python -- Debugging techniques -- Debuggers -- Best practices for high-quality Python programming -- Version control -- Debugging beyond Python -- Flaws in data used for modeling -- Data format and structure -- Data quantity and quality -- Data biases -- Model and prediction-centric debugging -- Underfitting and overfitting -- Inference in model testing and production -- Data or hyperparameters for changing landscapes -- Summary -- Questions -- References -- Chapter 2: Machine Learning Life Cycle -- Technical requirements -- Before we start modeling -- Data collection -- Data selection -- Data exploration -- Data wrangling -- Structuring -- Enriching -- Data transformation -- Cleaning -- Modeling data preparation -- Feature selection and extraction -- Designing an evaluation and testing strategy -- Model training and evaluation -- Testing the code and the model -- Model deployment and monitoring -- Summary -- Questions -- References -- Chapter 3: Debugging toward Responsible AI -- Technical requirements -- Impartial modeling fairness in machine learning -- Data bias -- Algorithmic bias -- Security and privacy in machine learning -- Data privacy -- Data poisoning -- Adversarial attacks -- Output integrity attacks -- System manipulation -- Secure and private machine learning techniques -- Transparency in machine learning modeling…”

Tabla de Contenidos: “…Gas chromatography and multivariate analysis for wheat flours -- 6.1 Introduction -- 6.2 Wheat grain compositions -- 6.3 Wheat flour -- 6.4 The standard for wheat flour -- 6.5 Quality assessment of wheat and wheat-based products -- 6.6 Application of gas chromatography coupled with mass spectroscopy -- 6.6.1 Gas chromatography coupled with mass spectroscopy analytical procedures -- 6.6.1.1 Extraction techniques -- 6.6.1.2 Derivatization -- 6.6.1.3 Chromatographic techniques -- 6.6.1.4 GC-MS data processing and statistical analysis -- 6.7 Application of gas chromatography coupled with mass spectroscopy and multivariate analysis in quality analysis of wheat... -- 6.7.1 Gas chromatography coupled with mass spectroscopy in analysis of wheat components/chemical composition -- 6.7.2 Gas chromatography coupled with mass spectroscopy analysis of volatile organic compounds in wheat flour and wheat-bas... -- 6.8 Gas chromatography coupled with mass spectroscopy-based metabolomics in wheat study -- 6.9 Gas chromatography coupled with mass spectroscopy wheat authentication -- 6.10 Gas chromatography coupled with mass spectroscopy in wheat and wheat-based food spoilage and storage…”

Tabla de Contenidos: “…Scenario Modeling -- Experimental Results -- Conclusions -- References -- Humanoid Motion Planning in the Goal Reaching Movement of Anthropomorphic Upper Limb -- Introduction -- Method -- Twists and Wrenches -- Second Order Kinematic Model -- Numerical Simulation -- Discussion and Conclusion -- References -- Human Sitting Posture Exposed to Horizontal Perturbation and Implications to Robotic Wheelchairs -- Introduction -- Biomechanics and Motor Control of Sitting Posture -- Vibrational Analysis of Sitting Posture -- Biomechanics and Control Model -- Implications to Robotic Wheelchairs -- Conclusions -- References -- Automatic Circumference Measurement for Aiding in the Estimation of Maximum Voluntary Contraction (MVC) in EMG Systems -- Introduction -- Background -- Design of Automatic Circumference Armband -- Experimental Results and Analysis -- Discussion -- Conclusion and Future Work -- References -- Classification of the Action Surface EMG Signals Based on the Dirichlet Process Mixtures Method -- Introduction -- Methods -- Dirichlet Process Mixture Model -- Feature Extraction -- Materials -- Analysis and Results -- Conclusions -- References -- Displacement Estimation for Foot Rotation Axis Using a Stewart-Platform-Type Assist Device -- Introduction -- Stewart-Platform-Type Ankle-Foot Assist Device -- Control the Posture (Inverse Kinematics) -- Measure the Posture (Forward Kinematics) -- Rotation Axis Estimation of Ankle Joint -- Ankle Joint -- Rotation Axis Estimation -- Experiment and Result -- Conclusions -- References -- Mechanisms and their Applications -- Inverse Kinematics Solution of a Class of Hybrid Manipulators -- Introduction -- Overview of Kinematic Configuration and Modeling -- Inverse Kinematics Solution -- A Numerical Example -- Discussion and Conclusion -- References -- Stiffness Analysis of Clavel's DELTA Robot…”

Tabla de Contenidos: “…4.2.2 The Wyner / Ziv Theorem 110 -- 4.2.3 DVC Codec Architecture 111 -- 4.2.4 Input Bitstream Preparation / Quantization and Bit Plane Extraction 112 -- 4.2.5 Turbo Encoder 112 -- 4.2.6 Parity Bit Puncturer 114 -- 4.2.7 Side Information 114 -- 4.2.8 Turbo Decoder 115 -- 4.2.9 Reconstruction: Inverse Quantization 116 -- 4.2.10 Key Frame Coding 117 -- 4.3 Stopping Criteria for a Feedback Channel-based Transform Domain Wyner / Ziv Video Codec 118 -- 4.3.1 Proposed Technical Solution 118 -- 4.3.2 Performance Evaluation 120 -- 4.4 Rate-distortion Analysis of Motion-compensated Interpolation at the Decoder in Distributed Video Coding 122 -- 4.4.1 Proposed Technical Solution 122 -- 4.4.2 Performance Evaluation 126 -- 4.5 Nonlinear Quantization Technique for Distributed Video Coding 129 -- 4.5.1 Proposed Technical Solution 129 -- 4.5.2 Performance Evaluation 132 -- 4.6 Symmetric Distributed Coding of Stereo Video Sequences 134 -- 4.6.1 Proposed Technical Solution 134 -- 4.6.2 Performance Evaluation 137 -- 4.7 Studying Error-resilience Performance for a Feedback Channel-based Transform Domain Wyner / Ziv Video Codec 139 -- 4.7.1 Proposed Technical Solution 139 -- 4.7.2 Performance Evaluation 140 -- 4.8 Modeling the DVC Decoder for Error-prone Wireless Channels 144 -- 4.8.1 Proposed Technical Solution 145 -- 4.8.2 Performance Evaluation 149 -- 4.9 Error Concealment Using a DVC Approach for Video Streaming Applications 151 -- 4.9.1 Proposed Technical Solution 152 -- 4.9.2 Performance Evaluation 155 -- 4.10 Conclusions 158 -- References 159 -- 5 Non-normative Video Coding Tools 161 -- 5.1 Introduction 161 -- 5.2 Overview of the State of the Art 162 -- 5.2.1 Rate Control 162 -- 5.2.2 Error Resilience 164 -- 5.3 Rate Control Architecture for Joint MVS Encoding and Transcoding 165 -- 5.3.1 Problem Definition and Objectives 165 -- 5.3.2 Proposed Technical Solution 166 -- 5.3.3 Performance Evaluation 169 -- 5.3.4 Conclusions 171 -- 5.4 Bit Allocation and Buffer Control for MVS Encoding Rate Control 171.…”

Tabla de Contenidos: “…Assessing Validity of Extracted Argument Patterns Via Dialectical Analysis -- 11.6. …”

Tabla de Contenidos: “…Activity 5.02: Building a Movie Genre Tagging a Model with TensorFlow -- Summary -- Chapter 6: Regularization and Hyperparameter Tuning -- Introduction -- Regularization Techniques -- L1 Regularization -- L2 Regularization -- Exercise 6.01: Predicting a Connect-4 Game Outcome Using the L2 Regularizer -- Dropout Regularization -- Exercise 6.02: Predicting a Connect-4 Game Outcome Using Dropout -- Early Stopping -- Activity 6.01: Predicting Income with L1 and L2 Regularizers -- Hyperparameter Tuning -- Keras Tuner -- Random Search -- Exercise 6.03: Predicting a Connect-4 Game Outcome Using Random Search from Keras Tuner -- Hyperband -- Exercise 6.04: Predicting a Connect-4 Game Outcome Using Hyperband from Keras Tuner -- Bayesian Optimization -- Activity 6.02: Predicting Income with Bayesian Optimization from Keras Tuner -- Summary -- Chapter 7: Convolutional Neural Networks -- Introduction -- CNNs -- Image Representation -- The Convolutional Layer -- Creating the Model -- Exercise 7.01: Creating the First Layer to Build a CNN -- Pooling Layer -- Max Pooling -- Average Pooling -- Exercise 7.02: Creating a Pooling Layer for a CNN -- Flattening Layer -- Exercise 7.03: Building a CNN -- Image Augmentation -- Batch Normalization -- Exercise 7.04: Building a CNN with Additional Convolutional Layers -- Binary Image Classification -- Object Classification -- Exercise 7.05: Building a CNN -- Activity 7.01: Building a CNN with More ANN Layers -- Summary -- Chapter 8: Pre-Trained Networks -- Introduction -- ImageNet -- Transfer Learning -- Exercise 8.01: Classifying Cats and Dogs with Transfer Learning -- Fine-Tuning -- Activity 8.01: Fruit Classification with Fine-Tuning -- TensorFlow Hub -- Feature Extraction -- Activity 8.02: Transfer Learning with TensorFlow Hub -- Summary -- Chapter 9: Recurrent Neural Networks -- Introduction -- Sequential Data…”

Tabla de Contenidos: “…Assessment and recommendations -- Introduction -- Overview of Indonesia' development path -- Democratisation and decentralisation have progressed albeit not without challenges -- Until the pandemic, economic growth had been solid and steady -- The manufacturing sector has been steadily declining as a share of GDP, but there is a gradual shift from the extractive industry to services -- Continued growth has reduced poverty, but improving the quality and competitiveness of human resources is necessary -- Improving the business environment stands high on the government's agenda -- The government seeks to addresses infrastructure gaps impeding business environment improvements -- Despite a recent reduction of the deforestation rate, pollution and deforestation still threaten sustainability -- FDI has played an important role in Indonesia but can further contribute to sustainable development -- Indonesia has the potential to be a key FDI destination in ASEAN, but investment climate reforms will make it more competitive -- FDI contributes to sustainable development but its impact can be enhanced -- Key findings and recommendations to improve Indonesia's investment climate -- Indonesia's approach towards FDI needs to be more open -- Main policy recommendations -- Indonesia's investment protection and dispute resolution have improved but need further reforms to build investor confidence -- Main policy recommendations for the domestic legal framework -- Main policy recommendations for investment treaty policy -- Embracing promotion of responsible business conduct can lead to far-reaching and strategic successes in attracting FDI and promoting a more sound and sustainable investment climate -- Main policy recommendations on responsible business conduct…”

Tabla de Contenidos: “…-- Implementing an achievement system -- Example of usage -- Taking in the view with dashboards -- There is no I in team -- Adding an element of fun to agile frameworks -- Summary -- Chapter 4: Organized Chaos - Getting Ideas Out of Your Head and on to Paper -- Brainstorming - getting it all out on the table -- Tools and methods for getting ideas out and organizing thoughts -- Start pinning with Pinterest -- Starting with a fresh slate -- Never forget with Evernote -- Brainstorming activities -- Spinning the wheel of randomness -- Getting in and among it -- Game on with Gamicards -- The great exchange -- Getting moody with mood boards -- Creating the ideal ideation sessions -- Implementing a brainstorming tool in Unity to shuffle ideas -- Making a new friend with editor scripts -- Creating an editor script -- Opening a new editor window -- Drawing something inside our editor window -- Adding a scrollable text area -- Inserting a button and implementing the shuffle function -- Showing the extracted idea -- Testing our brainstorming tool -- Summary -- Chapter 5: Sculpting the Conceptual Beast -- Creating a Game Design Document -- Google Drive -- Word (and Microsoft Office) -- InDesign -- Creating a prototype - what you'll need -- Gathering your humans -- Having a plan! …”

Tabla de Contenidos: “…Analyzing missing data -- Combining data using a JOIN operation -- Munging textual data -- Processing multiple input data files -- Removing stop words -- Munging time series data -- Pre-processing of the&amp -- #160 -- time-series Dataset -- Processing date fields -- Persisting and loading data -- Defining a date-time index -- Using the&amp -- #160 -- &amp -- #160 -- TimeSeriesRDD&amp -- #160 -- object -- Handling missing time-series data -- Computing basic statistics -- Dealing with variable length records -- Converting variable-length records to fixed-length records -- Extracting data from "messy" columns -- Preparing data for machine learning -- Pre-processing data for machine learning -- Creating and running a machine learning pipeline -- Summary -- Chapter 5: Using Spark SQL in Streaming Applications -- Introducing streaming data applications -- Building Spark streaming applications -- Implementing sliding window-based functionality -- Joining a streaming Dataset with a static Dataset -- Using the Dataset API in Structured Streaming -- Using output sinks -- Using the Foreach Sink for arbitrary computations on output -- Using the Memory Sink to save output to a table -- Using the File Sink to save output to a partitioned table -- Monitoring streaming queries -- Using Kafka with Spark Structured Streaming -- Introducing Kafka concepts -- Introducing ZooKeeper concepts -- Introducing Kafka-Spark integration -- Introducing Kafka-Spark Structured Streaming -- Writing a receiver for a custom data source -- Summary -- Chapter 6: Using Spark SQL in Machine Learning Applications -- Introducing machine learning applications -- Understanding Spark ML pipelines and their components -- Understanding the steps in a pipeline application development process -- Introducing feature engineering -- Creating new features from raw data…”

Tabla de Contenidos: “…. -- Chapter 6: Working with Files, Streams, and Serialization -- Introduction -- Creating and extracting ZIP archives -- Getting ready -- How to do it... -- How it works... -- In-memory stream compression and decompression -- Getting ready -- How to do it... -- How it works... -- Async and await file processing -- Getting ready -- How to do it... -- How it works... -- How to make a custom type serializable? …”

Tabla de Contenidos: “…Chapter 6: Advanced Query Writing -- Common table expressions -- Reusing SQL code with CTE -- Recursive and hierarchical queries -- Changing data in multiple tables at a time -- Window functions -- Window definition -- The WINDOW clause -- Using window functions -- Window functions with grouping and aggregation -- Advanced SQL techniques -- Selecting the first records -- Selecting a data sample -- Set returning functions -- Lateral subqueries -- Advanced grouping -- Advanced aggregation -- Summary -- Chapter 7: Server-Side Programming with PL/pgSQL -- SQL language and PL/pgSQL - a comparison -- PostgreSQL function parameters -- Function authorization-related parameters -- Function planner-related parameters -- Function configuration-related parameters -- The PostgreSQL PL/pgSQL control statements -- Declaration statements -- Assignment statements -- Conditional statements -- Iteration -- Loop statement -- While loop statement -- For loop statement -- Returning from the function -- Returning void -- Returning a single row -- Returning multiple rows -- Function predefined variables -- Exception handling -- Dynamic SQL -- Executing DDL statements in dynamic SQL -- Executing DML statements in dynamic SQL -- Dynamic SQL and the caching effect -- Recommended practices for dynamic SQL usage -- Summary -- Chapter 8: OLAP and Data Warehousing -- Online analytical processing -- Extract, transform, and load -- Data modeling for OLAP -- Aggregation -- Partitioning -- Parallel query -- Index-only scans -- Summary -- Chapter 9: Beyond Conventional Data Types -- Arrays -- Common functions of arrays and their operators -- Modifying and accessing arrays -- Indexing arrays -- The hash store data structure -- Indexing an hstore -- The JSON data structure -- JSON and XML -- JSON data types for PostgreSQL -- Modifying and accessing JSON types -- Indexing a JSON data type…”

Tabla de Contenidos: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Marketing with AI -- Chapter 1 A Brief History of AI -- Early Technological Advances -- Alan Turing and Machine Intelligence -- The Turing Test in 1950 -- The Turing test: 1960s and beyond -- The Dartmouth Conference of 1956 -- Machine Learning and Expert Systems Emerge -- Meeting machine learning -- Examining expert systems -- An AI Winter Sets In -- The Stanford Cart: From the '60s to the '80s -- More AI Developments in the 1980s -- Rapid Advancements of AI in the 1990s and Beyond -- Watching machine learning grow up -- Playing a pivotal chess match -- Tracking the deep learning revolution -- Demonstrating intuition in the age of AI -- Creating content with generative AI -- Chapter 2 Exploring AI Business Use Cases -- Automating Customer Service -- Serving customers by using chatbots -- Resolving customer issues with virtual assistants -- Seeking out trends and solutions with sentiment analysis -- Enhancing Product and Technology with AI -- Streamlining product validation -- Simulating user experience testing -- Writing code -- Detecting and resolving software bugs -- Testing software and creating documentation -- Accelerating Research and Development -- Generating and exploring ideas -- Extracting insights from data -- Optimizing product designs and production processes -- Giving Marketing an AI Boost -- Creating coherent, consistent content -- Personalizing marketing messages -- Managing digital advertising -- Streamlining search engine optimization (SEO) -- Optimizing Sales with AI -- Driving profitability -- Nurturing leads -- Forecasting sales -- Adding AI to Legal Activities -- Analyzing documentation for legal research…”

Tabla de Contenidos: “…-- Use cases -- Web apps -- IOT and mobile backends -- Extract, Transform, Load (ETL) -- Data processing -- Real-time processing -- Execution environment -- Environment variables -- Execution context -- AWS Lambda Function-Hello World -- Function code - configuration -- Publishing and testing your changes -- Configuring options for AWS Lambda -- Memory configuration -- Execution time configuration -- Network configuration -- Function versioning and aliases -- Traffic Shifting using aliases -- Environment variables -- Securing AWS Lambda using IAM -- Authenticating -- Access control -- Identity-based policies (IAM policies) -- Resource-based policies (Lambda function policies) -- AWS Lambda permissions model -- Summary -- Chapter 4: Triggers and Events for AWS Lambda -- Triggers for AWS Lambda -- API Gateway Trigger -- AWS IoT Trigger -- IoT Button -- IoT rule -- Alexa trigger -- CloudFront trigger -- CloudWatch trigger -- CloudWatch Events -- CloudWatch Logs -- CodeCommit Trigger -- Cognito Trigger -- Scheduled Events Trigger -- AWS Config Trigger -- DynamoDB Trigger -- Kinesis Trigger -- Amazon Kinesis Data Streams -- Amazon Kinesis Data Firehose -- S3 Trigger…”