Tabla de Contenidos: “…An introduction to basic concepts on recommender systems / Pooja Rana, Nishi Jain and Usha Mittal -- A brief model overview of personalized recommendation to citizens in the health-care industry / Subhasish Mohapatra and Kunal Anand -- 2Es of TIS : a review of information exchange and extraction in tourism information systems / Malik M. …”

Tabla de Contenidos: “…Intro -- Cover -- Title -- Contents -- Introduction -- Tapputi-Belatikallim: Fragrance Distillation -- Maria Sibylla Merian: Biological Illustration/Metamorphosis -- Laura Bassi: Air/Electricity -- Eunice Newton Foote: Greenhouse Gases -- Fanny Hesse: Agar Growth Median -- Agnes Pockels: Surface Tension -- Marie Curie: Elemental Extraction -- Susan La Flesche Picotte: Public Health/Houseflies -- Mary Agnes Chase: Agrostology/Grasses -- Ynés Mexía: Plant Collection/Identification -- Lise Meitner: Nuclear Fission -- Alice Ball: Organic Separation -- Gerty Cori: The Cori Cycle -- Katharine Burr Blodgett: Thin Films -- Cecilia Payne-Gaposchkin: Star Material -- Dora Priaulx Henry: Barnacles -- Maria Goeppert-Mayer: Nuclear Shell Model -- Mary Golda Ross: Aerospace Engineering -- Rachel Carson: Environmental Contaminants -- Chien-Shiung Wu: Symmetry -- Anna Jane Harrison: Organic Compounds/Ultraviolet Light -- Ruby Payne-Scott: Sunspots -- Rosalind Franklin: DNA Structure -- Margaret S. …”

“…At the same time, powerful narratives about the drylands as 'wastelands' and their 'backward' inhabitants continue to hold sway, legitimizing interventions for development, security and conservation informing re-emerging frontiers of investment (for agriculture, extraction, infrastructure), and shaping new dryland identities. …”

“…How marketers learned to dream of optimization and speak in the idiom of management science well before the widespread use of the Internet. Algorithms, data extraction, digital marketers monetizing "eyeballs": these all seem like such recent features of our lives. …”

“…Covering Norway, Sweden and Finland - three countries with many differences and similarities - this volume examines how reindeer husbandry is affected by and responds to global environmental change and resource extraction in boreal and arctic social-ecological systems. …”

“…You'll review metaverse services XR applications and learn more about the core XR feature extraction technologies. With XR capabilities mastered, you can move into the main technologies for video streaming services like Netflix, Disney+, and YouTube. …”

“…Securing efficient results via the combination of symbolic AI and statistical AI such as entity extraction based on machine learning, text mining methods, semantic knowledge graphs, and related reasoning power, this book is the first of its kind to explore Semantic AI and Knowledge Graphs. …”

Tabla de Contenidos: “…Expected performance -- Putting it all together -- Extracting features generated by Inception V3 -- Preparing the network -- Removing the last Softmax and FullyConnected layers -- Predicting features of an image -- Saving the network to disk -- Extracting features generated by MobileNet V2 -- Preparing the network -- Removing the last Softmax and FullyConnected layers -- Predicting features of an image -- Saving the network to disc -- Putting it all together -- Transfer learning with Inception V3 -- Getting the data -- Preparing the dataset -- Extracting features -- Creating a new network -- Training and validating the results -- Summary -- Questions -- Further reading -- Chapter 8: OpenCV -- Technical requirements -- Troubleshooting installation of Open CV -- Troubleshooting installation on macOS -- First steps with OpenCV -- Updating OpenCV package source code -- Defining Open CV location -- Testing whether OpenCV works -- Working with images -- Converting OpenCV Mat to Julia images -- Reading images -- Saving images -- Destroying the object -- Image capturing from web camera -- Face detection using Open CV -- Object detection using MobileNet-SSD -- Summary -- Questions -- Appendix: Assessments -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- How to Use This Book -- Chapter 1: Working with Data -- Conversion Between Data Types -- Safe Number Conversion -- Conversion from Number to String and Back -- Boolean Conversions -- String to Data and Back -- Dates, Timestamps, and ISO 8601 Format -- Extracting Data from Dictionaries and Arrays -- Complex Data Types -- Extracting Data from Dictionary -- Extracting Data from Array -- Summary -- Chapter 2: Working with JSON and Other Popular Formats -- How Devices Exchange Data -- What Are Serialization and Deserialization -- Working with JSON -- Parsing JSON to Dictionary or Array -- Generating JSON from Dictionary or Array -- Working with XML -- Parsing XML -- Generating XML -- Working with YAML -- URL-Encoded Strings -- Serialization and Deserialization -- Serializing an Object -- Deserializing an Object -- Coding Keys -- Using @propertyWrapper -- Unit Conversion -- Distance and Length Units -- Area Units -- Volume Units -- Weight Units -- Summary -- Chapter 3: Working with Strings -- Analyzing String Content -- First, Last, and Other Names -- Email Address -- Password -- Phone Number -- Gender -- Date of Birth -- Credit Card Information -- Base64 and Hex Encoding -- Base64 Encoding -- Hex Encoding -- MD5, SHA, and Other Hashes -- MD5 Hash -- SHA Hashes -- Hashable Protocol and Swift Hashes -- Salt -- Integer Indexing -- Localization -- NSLocalizedString Macro -- Files with Translations -- LocalizedStringKey Struct -- Syntactic Sugar -- Summary -- Chapter 4: UIKit and Storyboards -- Navigation Between App Screens -- Screens, Windows, and Views -- Navigation in iOS Apps -- Scenes -- Going Back -- Replacing Root View -- Modifying UINavigationController Stack -- Removing Elements from the Navigation Stack…”

Tabla de Contenidos: “…Cover -- Contents -- Acknowledgements -- Academic Advisory Board -- General Introduction -- The Anthropocene as Multiple Crisis -- Periods of the Anthropocene's Genealogy in Latin America -- Colonial Period -- From the Mid‐Nineteenth Century to 1950 -- From 1950 to the Present -- Anthropocene Regions in Latin America -- Southern Cone -- Andes -- Amazon -- Mesoamerica -- Caribbean -- Land Use -- Final Words -- Colonial Period -- Introduction: Land Use in Colonial Latin America in the Anthropocene History -- References -- Land Use in the Southern Cone in the Colonial Period -- Demographic Change -- Changes in Land Use from an Indigenous Perspective -- Land Use for Food and Commerce -- Location, Resources, and Imperial Strategy -- Conclusion -- References -- Land Use in the Andes in the Colonial Period -- The Andean Space in History: Geosystems and Cultures -- The Impact of the Conquest and Changes in Land Ownership -- Royal Lands, Grants, Reductions, and Compositions -- Technological Changes and New Products -- The New Livestock Farming -- The Impact of Colonial Mining on the Landscape and the Environment -- The Export Market, Plantation Systems, and Cinchona Extraction -- Conclusions -- References -- Land Use in the Amazon in the Colonial Period -- Representations of the Conquest -- Colonization Projects: Missions and Cities (Seventeenth Century) -- Enlightenment Projects (Eighteenth Century) -- Land Use -- Environmental Impacts -- Conclusions -- References -- Land Use in Mesoamerica in the Colonial Period -- Mesoamerica: Imprints of the Future of a Region in the Long Term -- Land Use at the Time of Conquest -- Land‐use Actors and Transformation Factors -- Land, Property, and Labor: Setting and Implementing Crown Policies -- Resistance and Rebellions in the 300 Years of Colonialism in New Spain…”

Tabla de Contenidos: “…Review Questions -- Chapter 5 Data Processing and Preparation -- Data Transformation -- Encoding -- Scaling and Normalization -- Transformation Functions -- Structural Transformation -- Feature Extraction -- Data Enrichment and Augmentation -- Ground Truth Labeling -- Feature Engineering -- Merging and Combining Data -- Data Cleaning -- Identifying Data Errors -- Handling Inconsistent Data -- Addressing Duplicate Data -- Resolving Missing Data -- Dealing with Outliers -- Handling Class Imbalance -- Undersampling -- Oversampling -- Summary -- Exam Essentials -- Review Questions -- Chapter 6 Modeling and Evaluation -- Types of Models -- Regressors -- Classifiers -- Temporal Models -- Model Design Concepts -- The Holdout Method -- The Bias-Variance Trade-off -- Feature Selection -- Cross-Validation -- Bootstrapping -- Hyperparameter Tuning -- Model Evaluation -- Regressor Performance Metrics -- Classifier Performance Metrics -- Summary -- Exam Essentials -- Review Questions -- Chapter 7 Model Validation and Deployment -- Model Validation -- Performance Metrics -- Inference Performance -- Design Constraints -- Business Requirements Alignment -- Benchmarking -- Communicating Results -- Data -- Visuals -- Stakeholders -- Ethics -- Accessibility -- Documentation -- Model Deployment -- Containerization -- Virtualization -- Cluster Deployment -- Cloud Deployment -- On-Premises Deployment -- Hybrid Deployment -- Edge Deployment -- Machine Learning Operations (MLOps) -- Automation -- Versioning -- Testing -- Monitoring -- Summary -- Exam Essentials -- Review Questions -- Chapter 8 Unsupervised Machine Learning -- Association Rules -- Identifying Strong Rules -- Clustering -- Centroid-Based Clustering -- Connectivity-Based Clustering -- Density-Based Clustering -- Dimensionality Reduction -- Feature Extraction -- Recommender Systems -- Collaborative Filtering…”

Tabla de Contenidos: “…The Execution Trace &amp -- Logs Extraction -- 4.2.2. Test Cases Partitioning -- 4.3. …”

Tabla de Contenidos: “…Visualizing the goodness of fit -- Computing MSE and median absolute error -- Evaluating clusters with the mean silhouette coefficient -- Comparing results with a dummy classifier -- Determining MAPE and MPE -- Comparing with a dummy regressor -- Calculating the mean absolute error and the residual sum of squares -- Examining the kappa of classification -- Taking a look at the Matthews correlation coefficient -- Chapter 11: Analyzing Images -- Introduction -- Setting up OpenCV -- Applying Scale-Invariant Feature Transform (SIFT) -- Detecting features with SURF -- Quantizing colors -- Denoising images -- Extracting patches from an image -- Detecting faces with Haar cascades -- Searching for bright stars -- Extracting metadata from images -- Extracting texture features from images -- Applying hierarchical clustering on images -- Segmenting images with spectral clustering -- Chapter 12: Parallelism and Performance -- Introduction -- Just-in-time compiling with Numba -- Speeding up numerical expressions with Numexpr -- Running multiple threads with the threading module -- Launching multiple tasks with the concurrent.futures module -- Accessing resources asynchronously with the asyncio module -- Distributed processing with execnet -- Profiling memory usage -- Calculating the mean, variance, skewness, and kurtosis on the fly -- Caching with a least recently used cache -- Caching HTTP requests -- Streaming counting with the Count-min sketch -- Harnessing the power of the GPU with OpenCL -- Appendix A: Glossary -- Appendix B: Function Reference -- IPython -- Matplotlib -- NumPy -- pandas -- Scikit-learn -- SciPy -- Seaborn -- Statsmodels -- Appendix C: Online Resources -- IPython notebooks and open data -- Mathematics and statistics -- Appendix D: Tips and Tricks for Command-Line and Miscellaneous Tools -- IPython notebooks -- Command-line tools…”

Tabla de Contenidos: “…. -- Reading contents from text files all at once using Apache Commons IO -- Getting ready -- How to do it... -- Extracting PDF text using Apache Tika -- Getting ready -- How to do it... -- Cleaning ASCII text files using Regular Expressions -- How to do it... -- Parsing Comma Separated Value (CSV) Files using Univocity -- Getting ready -- How to do it... -- Parsing Tab Separated Value (TSV) file using Univocity -- Getting ready -- How to do it... -- Parsing XML files using JDOM -- Getting ready -- How to do it... -- Writing JSON files using JSON.simple -- Getting ready -- How to do it... -- Reading JSON files using JSON.simple -- Getting ready -- How to do it ... -- Extracting web data from a URL using JSoup -- Getting ready -- How to do it... -- Extracting web data from a website using Selenium Webdriver -- Getting ready -- How to do it... -- Reading table data from a MySQL database -- Getting ready -- How to do it... -- Chapter 2: Indexing and Searching Data -- Introduction -- Indexing data with Apache Lucene -- Getting ready -- How to do it... -- How it works... -- Searching indexed data with Apache Lucene -- Getting ready -- How to do it... -- Chapter 3: Analyzing Data Statistically -- Introduction -- Generating descriptive statistics -- How to do it... -- Generating summary statistics -- How to do it... -- Generating summary statistics from multiple distributions -- How to do it... -- There's more...…”

Tabla de Contenidos: “…Exploring different types of regression -- Introducing linear regression -- Linear regression model in MATLAB -- Making predictions with regression analysis in MATLAB -- Multiple linear regression with categorical predictor -- Evaluating model performance -- Reducing outlier effects -- Using advanced techniques for model evaluation and selection in MATLAB -- Understanding k-fold cross-validation -- Exploring leave-one-out cross-validation -- Introducing the bootstrap method -- Summary -- Chapter 4: Clustering Analysis and Dimensionality Reduction -- Technical requirements -- Understanding clustering - basic concepts and methods -- How to measure similarity -- How to find centroids and centers -- How to define a grouping -- Understanding hierarchical clustering -- Partitioning-based clustering algorithms with MATLAB -- Introducing the k-means algorithm -- Using k-means in MATLAB -- Grouping data using the similarity measures -- Applying k-medoids in MATLAB -- Discovering dimensionality reduction techniques -- Introducing feature selection methods -- Exploring feature extraction algorithms -- Feature selection and feature extraction using MATLAB -- Stepwise regression for feature selection -- Carrying out PCA -- Summary -- Chapter 5: Introducing Artificial Neural Network Modeling -- Technical requirements -- Getting started with ANNs -- Basic concepts relating to ANNs -- Understanding how perceptrons work -- Activation function to introduce non-linearity -- ANN's architecture explained -- Training and testing an ANN model in MATLAB -- How to train an ANN -- Introducing the MATLAB Neural Network Toolbox -- Understanding data fitting with ANNs -- Discovering pattern recognition using ANNs -- Building a clustering application with an ANN -- Exploring advanced optimization techniques -- Understanding SGD -- Exploring Adam optimization…”

Tabla de Contenidos: “…4.1 Overview of a DataFrame -- 4.1.1 Creating a DataFrame from a dictionary -- 4.1.2 Creating a DataFrame from a NumPy ndarray -- 4.2 Similarities between Series and DataFrames -- 4.2.1 Importing a DataFrame with the read_csv function -- 4.2.2 Shared and exclusive attributes of Series and DataFrames -- 4.2.3 Shared methods of Series and DataFrames -- 4.3 Sorting a DataFrame -- 4.3.1 Sorting by a single column -- 4.3.2 Sorting by multiple columns -- 4.4 Sorting by index -- 4.4.1 Sorting by row index -- 4.4.2 Sorting by column index -- 4.5 Setting a new index -- 4.6 Selecting columns and rows from a DataFrame -- 4.6.1 Selecting a single column from a DataFrame -- 4.6.2 Selecting multiple columns from a DataFrame -- 4.7 Selecting rows from a DataFrame -- 4.7.1 Extracting rows by index label -- 4.7.2 Extracting rows by index position -- 4.7.3 Extracting values from specific columns -- 4.8 Extracting values from Series -- 4.9 Renaming columns or rows -- 4.10 Resetting an index -- 4.11 Coding challenge -- 4.11.1 Problems -- 4.11.2 Solutions -- Summary -- 5 Filtering a DataFrame -- 5.1 Optimizing a data set for memory use -- 5.1.1 Converting data types with the astype method -- 5.2 Filtering by a single condition -- 5.3 Filtering by multiple conditions -- 5.3.1 The AND condition -- 5.3.2 The OR condition -- 5.3.3 Inversion with ~ -- 5.3.4 Methods for Booleans -- 5.4 Filtering by condition -- 5.4.1 The isin method -- 5.4.2 The between method -- 5.4.3 The isnull and notnull methods -- 5.4.4 Dealing with null values -- 5.5 Dealing with duplicates -- 5.5.1 The duplicated method -- 5.5.2 The drop_duplicates method -- 5.6 Coding challenge -- 5.6.1 Problems -- 5.6.2 Solutions -- Summary -- Part 2. …”

Tabla de Contenidos: “…-- Performing naïve Bayes classification -- Classification metrics -- Understanding decision trees -- Measuring purity -- Exploring the Titanic dataset -- Dummy variables -- Diving deep into UL -- When to use UL -- k-means clustering -- The Silhouette Coefficient -- Feature extraction and PCA -- Summary -- Chapter 12: Introduction to Transfer Learning and Pre-Trained Models -- Understanding pre-trained models -- Benefits of using pre-trained models -- Commonly used pre-trained models -- Decoding BERT's pre-training -- TL -- Different types of TL -- Inductive TL -- Transductive TL -- Unsupervised TL - feature extraction -- TL with BERT and GPT -- Examples of TL -- Example - Fine-tuning a pre-trained model for text classification -- Summary -- Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift -- Understanding algorithmic bias -- Types of bias -- Sources of algorithmic bias -- Measuring bias -- Consequences of unaddressed bias and the importance of fairness -- Mitigating algorithmic bias -- Mitigation during data preprocessing -- Mitigation during model in-processing -- Mitigation during model postprocessing -- Bias in LLMs -- Uncovering bias in GPT-2 -- Emerging techniques in bias and fairness in ML -- Understanding model drift and decay -- Model drift -- Data drift -- Mitigating drift -- Understanding the context -- Continuous monitoring -- Regular model retraining -- Implementing feedback systems -- Model adaptation techniques -- Summary -- Chapter 14: AI Governance -- Mastering data governance -- Current hurdles in data governance -- Data management: crafting the bedrock -- Data ingestion - the gateway to information -- Data integration - from collection to delivery -- Data warehouses and entity resolution…”

Tabla de Contenidos:

Tabla de Contenidos: “…3.3 - LPC Coefficient Substitution Based on Filter Similarity3.3.1 - LPC Substitution Algorithm; 3.3.2 - Multicodebook; 3.4 - Secret Speech Information Hiding and Extraction Algorithm; 3.4.1 - Speech Information Hiding Algorithm; 3.4.2 - Speech Information Extraction Algorithm; 3.5 - Experimental Results and Analysis; 3.5.1 - Selection of Test Parameters; 3.5.2 - Experimental Results; 3.5.3 - Calculation Complexity; 3.5.4 - Speech Quality; 3.6 - Summary; Chapter 4 - The G.721-Based Speech Information Hiding Approach; 4.1 - Introduction to the G.721 Coding Standard…”

Tabla de Contenidos: “…Vardas and Emmanuel M. Kanoupakis -- Extraction of pacing and defibrillation leads / Charles J. …”
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