Tabla de Contenidos: “…. -- Introduction -- The Palaeoenvironment -- Previous Studies at Formby Point -- Footprint Formation and Preservation Process -- Blundell Path C -- The Footprints in the Bed -- Context 3, the Lowest Layer -- Context 2, the Middle Layer -- Context 1, the Top Layer -- Activity on the Mudflats -- Faunal Behaviour in the Intertidal Zone -- Humans in the Intertidal Zone -- Experience in the Intertidal Zone -- Evidence of Coastal Occupation -- Hunter-Gatherer-Foragers at Formby -- Conclusion -- References -- Chapter 17: Prehistoric Human Tracks in Ojo Guareña Cave System (Burgos, Spain): The Sala and Galerías de las Huellas -- Introduction -- The Site -- Access to a Complicated Sector -- Materials and Methods -- Footprint Documentation -- Footprints and Trackways -- Estimation of Height and Weight -- Chronology -- Results -- Footprints and Trackways -- Chronology -- Discussion -- Conclusion -- References -- Part III: Experiences with Indigenous Experts -- Chapter 18: Tracking with Batek Hunter-Gatherers of Malaysia -- Introduction -- Ethnographic Background -- Tracking Habitats -- Hunting, Animals, Tracks -- Encountering Forest Tracks -- Simple, Systematic, and Speculative Tracking -- Discussion -- References…”

Tabla de Contenidos: “…3.2.2 Indirect Method Sensors -- 3.2.3 Dynamometer -- 3.2.4 Accelerometer -- 3.2.5 Acoustic Emission Sensor -- 3.2.6 Current Sensors -- 3.3 Other Sensors -- 3.3.1 Temperature Sensors -- 3.3.2 Optical Sensors -- 3.4 Interaction of Sensors During Machining Operation -- 3.4.1 Milling Machining -- 3.4.2 Turning Machining -- 3.4.3 Drilling Machining Operation -- 3.5 Sensor Fusion Technique -- 3.6 Interaction of Internet of Things -- 3.6.1 Identification -- 3.6.2 Sensing -- 3.6.3 Communication -- 3.6.4 Computation -- 3.6.5 Services -- 3.6.6 Semantics -- 3.7 IoT Technologies in Manufacturing Process -- 3.7.1 IoT Challenges -- 3.7.2 IoT-Based Energy Monitoring System -- 3.8 Industrial Application -- 3.8.1 Integrated Structure -- 3.8.2 Monitoring the System Related to Service Based on Internet of Things -- 3.9 Decision Making Methods -- 3.9.1 Artificial Neural Network -- 3.9.2 Fuzzy Inference System -- 3.9.3 Support Vector Mechanism -- 3.9.4 Decision Trees and Random Forest -- 3.9.5 Convolutional Neural Network -- 3.10 Conclusion -- References -- Chapter 4 Application of Internet of Things (IoT) in the Automotive Industry -- 4.1 Introduction -- 4.2 Need For IoT in Automobile Field -- 4.3 Fault Diagnosis in Automobile -- 4.4 Automobile Security and Surveillance System in IoT-Based -- 4.5 A Vehicle Communications -- 4.6 The Smart Vehicle -- 4.7 Connected Vehicles -- 4.7.1 Vehicle-to-Vehicle (V2V) Communications -- 4.7.2 Vehicle-to-Infrastructure (V2I) Communications -- 4.7.3 Vehicle-to-Pedestrian (V2P) Communications -- 4.7.4 Vehicle to Network (V2N) Communication -- 4.7.5 Vehicle to Cloud (V2C) Communication -- 4.7.6 Vehicle to Device (V2D) Communication -- 4.7.7 Vehicle to Grid (V2G) Communications -- 4.8 Conclusion -- References -- Chapter 5 IoT for Food and Beverage Manufacturing -- 5.1 Introduction -- 5.2 The Influence of IoT in a Food Industry…”

“…A review of the requisite mathematics for growth modeling and the statistical techniques needed for estimating growth models are provided, and an overview of popular growth curves, such as linear, logarithmic, reciprocal, logistic, Gompertz, Weibull, negative exponential, and log-logistic, among others, is included.In addition, the book discusses key application areas including economic, plant, population, forest, and firm growth and is suitable as a resource for assessing recent growth modeling trends in the medical field. …”

Tabla de Contenidos: “…11.2.3 Day-to-Day Example -- 11.2.3.1 Optical Character Recognition (OCR) -- 11.2.3.2 Face Recognition -- 11.2.3.3 Recognition of Speech -- 11.2.3.4 Medical Findings -- 11.2.3.5 Extraction of Acquaintance -- 11.2.3.6 Compression -- 11.2.3.7 Additional Examples -- 11.2.4 Discriminant -- 11.2.5 Algorithms -- 11.3 Clustering -- 11.3.1 Data Examples Using Natural Clusters -- 11.4 Clustering (k-means) -- 11.4.1 Outline -- 11.4.2 Example -- 11.4.2.1 Problem -- 11.4.2.2 Solution -- 11.4.3 Some Methods for Initialization -- 11.4.4 Disadvantages -- 11.4.5 Use Case: Image Compression and Segmentation -- 11.4.5.1 Segmentation of Images -- 11.4.5.2 Compression of Data -- 11.5 Reduction of Dimensionality -- 11.5.1 Introduction -- 11.5.1.1 Feature Selection -- 11.5.1.2 Feature Extraction -- 11.5.1.3 Error Measures -- 11.5.2 Benefits of Reducing Dimensionality -- 11.5.3 Subset Selection -- 11.5.3.1 Selecting Forward -- 11.5.3.2 Remarks -- 11.5.3.3 Selection in Reverse -- 11.6 The Ensemble Method -- 11.6.1 Random Forest -- 11.6.2 Algorithm -- 11.6.3 Benefits and Drawbacks -- 11.6.3.1 Benefits -- 11.6.3.2 Drawbacks -- 11.6.4 Deep Learning and Neural Networks -- 11.6.4.1 Definition -- 11.6.4.2 Remarks -- 11.6.5 Applications -- 11.6.6 Artificial Neural Network -- 11.6.6.1 Biological Motivation -- 11.7 Transfer of Learning -- 11.8 Learning Through Reinforcement -- 11.9 Processing of Natural Languages -- 11.10 Word Embeddings -- 11.11 Conclusion -- References -- Chapter 12 Recognition Attendance System Ensuring COVID-19 Security -- 12.1 Introduction -- 12.2 Literature Survey -- 12.3 Software Requirements -- 12.3.1 Operating System - Windows 7 and Above -- 12.3.2 IDE-Visual Studio Code -- 12.3.3 Programming Languages: Python, HTML, CSS, JS, and PHP -- 12.4 Hardware Requirements -- 12.4.1 Three Processors and Above -- 12.4.2 RAM - 2GB (Minimum Capacity)…”

Tabla de Contenidos: “…3.2.2 Indirect Method Sensors -- 3.2.3 Dynamometer -- 3.2.4 Accelerometer -- 3.2.5 Acoustic Emission Sensor -- 3.2.6 Current Sensors -- 3.3 Other Sensors -- 3.3.1 Temperature Sensors -- 3.3.2 Optical Sensors -- 3.4 Interaction of Sensors During Machining Operation -- 3.4.1 Milling Machining -- 3.4.2 Turning Machining -- 3.4.3 Drilling Machining Operation -- 3.5 Sensor Fusion Technique -- 3.6 Interaction of Internet of Things -- 3.6.1 Identification -- 3.6.2 Sensing -- 3.6.3 Communication -- 3.6.4 Computation -- 3.6.5 Services -- 3.6.6 Semantics -- 3.7 IoT Technologies in Manufacturing Process -- 3.7.1 IoT Challenges -- 3.7.2 IoT-Based Energy Monitoring System -- 3.8 Industrial Application -- 3.8.1 Integrated Structure -- 3.8.2 Monitoring the System Related to Service Based on Internet of Things -- 3.9 Decision Making Methods -- 3.9.1 Artificial Neural Network -- 3.9.2 Fuzzy Inference System -- 3.9.3 Support Vector Mechanism -- 3.9.4 Decision Trees and Random Forest -- 3.9.5 Convolutional Neural Network -- 3.10 Conclusion -- References -- Chapter 4 Application of Internet of Things (IoT) in the Automotive Industry -- 4.1 Introduction -- 4.2 Need For IoT in Automobile Field -- 4.3 Fault Diagnosis in Automobile -- 4.4 Automobile Security and Surveillance System in IoT-Based -- 4.5 A Vehicle Communications -- 4.6 The Smart Vehicle -- 4.7 Connected Vehicles -- 4.7.1 Vehicle-to-Vehicle (V2V) Communications -- 4.7.2 Vehicle-to-Infrastructure (V2I) Communications -- 4.7.3 Vehicle-to-Pedestrian (V2P) Communications -- 4.7.4 Vehicle to Network (V2N) Communication -- 4.7.5 Vehicle to Cloud (V2C) Communication -- 4.7.6 Vehicle to Device (V2D) Communication -- 4.7.7 Vehicle to Grid (V2G) Communications -- 4.8 Conclusion -- References -- Chapter 5 IoT for Food and Beverage Manufacturing -- 5.1 Introduction -- 5.2 The Influence of IoT in a Food Industry…”

Tabla de Contenidos: “…Wallimann-Helmer -- 9. Ecofeminism, afforestation &amp -- La Via Campesina -- E. Woodhouse -- Section 3. …”

Tabla de Contenidos: “…Chapter 12 - Adjusting for AR(1) correlation in complex models 12.1 Introduction 12.2 The two sample t-test - Uncut and patch cut forest 12.3 The second Sleuth case - Global warming, a simple regression 12.4 The Semmelweis intervention 12.5 The NYC temperatures (adjusted) 12.6 The Boise river flow data: model selection with filtering 12.7 Implications of AR(1) adjustments and the "skip" method 12.8 Summary Part III - Complex temporal structures Chapter 13 - The backshift operator, the impulse response function, and general ARMA models 13.1 The general ARMA model 13.2 The backshift (shift, lag) operator 13.3 The impulse response operator - intuition 13.4 Impulse response operator, g(B) - computation 13.5 Interpretation and utility of the impulse response function Chapter 14 - The Yule-Walker equations and the partial autocorrelation function. 14.1 Background 14.2 Autocovariance of an ARMA(m,l) model 14.3 AR(m) and the Yule-Walker equations 14.4 The partial autocorrelation plot 14.5 The spectrum for ARMA processes 14.6 Summary Chapter 15 - Modeling philosophy and complete examples 15.1 Modeling overview 15.2 A complex periodic model - Monthly river flows, Furnas 1931-1978 15.3 A modeling example - trend and periodicity: CO2 levels at Mauna Lau 15.4 Modeling periodicity with a possible intervention - two examples 15.5 Periodic models: monthly, weekly, and daily averages 15.6 Summary Part IV - Some detailed and complete examples Chapter 16 - the Wolf sunspot number data 16.1 Background 16.2 Unknown period => nonlinear model 16.3 The function nls() in R 16.4 Determining the period 16.5 Instability in the mean, amplitude, and period 16.6 Data splitting for prediction 16.7 Summary Chapter 17 - Analysis of prostate and breast cancer data 17.1 Background 17.2 The first data set 17.3 The second data set Chapter 18 - Christopher Tennant/Ben Crosby watershed data 18.1 Background and question 18.2 Looking at the data and fitting Fourier series 18.3 Averaging data 18.4 Results Chapter 19 - Vostok ice core data 19.1 Source of the data 19.2 Background 19.3 Alignment 19.4 A naïve analysis 19.5 A related simulation 19.6 An AR(1) model for irregular spacing 19.7 Summary Appendices Appendix 1 - Using Data Market A1.1 Overview A1.2 Loading a time series in DataMarket A1.3 Respecting DataMarket licensing agreements Appendix 2 - AIC is PRESS A2.1 Introduction A2.2 PRESS A2.3 Connection to Akaike's result A2.4 Normalization and R2 A2.5 An example A2.6 Conclusion and further comments Appendix 3 - A 15 minute tutorial on optimization and nonlinear regression A3.1 Introduction A3.2 Newton's method for one dimensional nonlinear optimization A3.3 A direction, a step size, and a stopping rule A3.4 What could go wrong? …”

Tabla de Contenidos: “…-- Chapter 11 Environment and Population -- Interdependence and the Environment -- Sustainable Development -- Managing the Environment -- The Atmosphere -- Public Opinion and International Relations Climate Change -- Seeking the Collective Good Global Warming -- Biodiversity -- Forests and Oceans -- Policy Perspectives Prime Minister of Ireland, Leo Varadkar -- Pollution -- Natural Resources -- World Energy -- Minerals -- Water -- Population -- The Demographic Transition -- Population Policies -- Disease -- Chapter Review -- Let's Debate the Issue Stopping Global Warming: Who Should Pay? …”

Tabla de Contenidos: “…. -- Chapter 6: Building a Classification Model with Spark * -- Types of classification models -- Linear models -- Logistic regression -- Multinomial logistic regression -- Visualizing the StumbleUpon dataset -- Extracting features from the Kaggle/StumbleUpon evergreen classification dataset -- StumbleUponExecutor -- Linear support vector machines -- The naà ̄ve Bayes model -- Decision trees -- Ensembles of trees -- Random Forests -- Gradient-Boosted trees -- Multilayer perceptron classifier -- Extracting the right features from your data -- Training classification models…”

Tabla de Contenidos: “…Meta-Analysis -- The Forest Plot -- Does a Brain Picture Increase Credibility?…”

Tabla de Contenidos: “…Supply chain and inventory optimization -- Predictive maintenance -- Healthcare diagnostics and treatment -- The different types of machine learning -- Supervised learning -- Unsupervised learning -- Semi-supervised learning -- Reinforcement learning -- Transfer learning -- Popular machine learning algorithms -- Linear regression -- Logistic regression -- Decision trees -- Random forests -- Support vector machines -- k-nearest neighbors -- Neural networks -- The machine learning process -- Training a supervised machine learning model -- Validation of a supervised machine learning model -- Testing a supervised machine learning model -- Evaluating machine learning models -- Risks and limitations of machine learning -- Overfitting and underfitting -- Bias and variance -- Balanced dataset -- Models are approximations of reality -- Machine learning on unstructured data -- Natural language processing (NLP) -- Computer vision -- Deep learning and artificial intelligence -- Artificial intelligence -- Deep learning -- Summary -- Supervised Machine Learning -- Defining supervised learning -- Applications of supervised learning -- The two types of supervised learning -- Key factors in supervised learning -- Steps within supervised learning -- Data preparation - laying the foundation -- Algorithm selection - choosing the right tool -- Model training - learning from data -- Model evaluation - assessing performance -- Prediction and deployment - putting the model to work -- Characteristics of regression and classification algorithms -- Regression algorithms -- Classification algorithms -- Key considerations in supervised learning -- Evaluation metrics -- Applications of supervised learning -- Consumer goods -- Retail -- Manufacturing -- Summary -- Unsupervised Machine Learning -- Defining UL -- Practical examples of UL -- Steps in UL -- Step 1 - Data collection…”

“…More resources should be devoted to combating widespread illegal mining and deforestation. This Working Paper relates to the 2015 OECD Economic Survey of Indonesia (www.oecd.org/eco/surveys/economic-survey-indonesia.htm)…”

Tabla de Contenidos: “…Conséquences pour l'environnement des évolutions touchant le secteur forestier -- Superficie forestière et déforestation -- Figure 10.3. Évolution de la superficie forestière par type de forêt, 1995-2000 -- Encadré 10.2. …”

“…The Morava river with its forests is one of the most beautiful and ecologically valuable riverscapes feturing the richest biodiversity in all of Central Europe. …”

Tabla de Contenidos: “…. -- Cause Related Marketing -- Caux Round Table Principles -- Centre for Corporate Governance (Nairobi) -- CERES -- CH2 Building, Melbourne -- Chapman Report (2006) (Australia) -- Cheating -- Chief Executive Officer -- Chief Sustainability Officer -- Christianity and CSR -- Christine Parker -- Civil Regulation -- Climate Change -- Clinton Global Initiative -- Club of Rome -- Co-determination -- Co-operation -- Co-ownership -- Coalation of Environmentally -- Responsible Economies -- Code of 'best practice' and norms of behavior -- Colin Scott -- Collaborative Advantage -- Collective bargaining/trade unions -- Combined Assurance -- Combined Code (June 2008) -- Command and Control -- Commonwealth Association of CG -- Communicating with Stakeholders -- Communities of Practice -- Community -- Community activism -- Community of practice -- Community outrage -- Community relations -- Company Directors and CSR -- Competition -- Competitive advantage -- Compliance/Legal Compliance -- Compliant Finance -- Comply-or-explain -- Comprehensive Environmental Responses , Compensation and Liability Act -- Confucian Ethics -- Consumer Driven Corporate Responsibility -- Consumerism Consumers' protection -- Core Principles of CSR Approaches Corporate -- Corporate Activism -- Corporate Citizenship -- Corporate codes of conduct -- Corporate giving -- Corporate Governance -- Corporate Governance as a Tool for Alleviating -- Developmental Issues -- Corporate Governance Reporting -- Corporate killing -- Corporate manslaughter -- Corporate Mission, Vision and Values -- Corporate moral agency -- Corporate negligence -- Corporate outrage -- Corporate Political Connection -- Corporate Reputation -- Corporate Responsibility Index -- Corporate Responsibiliy Maturity -- Corporate Secretaries -- Corporate Social Entrepreneurship -- Corporate Social innovation -- Corporate Social -- Irresponsibility -- Corporate Social Marketing -- Corporate Social Opportunity -- Corporate Social Performance -- Corporate Social Performance Measurement -- Corporate social responsibility -- Corporate Social Responsibility Report -- Corporate Social Responsibility Strategy -- Corporate social responsiveness (Carroll, Frederick and Ackerman) -- Corporate Strategy -- Corporate Sustainability -- Corporation as Psychopath -- Corporatism -- Corruption and National Development -- Cost-benefit analysis -- Cradle to cradle -- Cradle to grave -- Critical reflection in corporate management -- Critiques of Corporate Social Responsibility -- Cross-cultural attitudes to CSR -- CSR and Africa -- CSR and Catholic Social Teaching -- CSR and Corruption -- CSR and Poverty -- CSR and Spirituality -- CSR Butterfly effect -- CSR Communication -- CSR Continuum - core business to broader goals -- CSR Europe -- CSR Evolutionary Journey - CSR Journey, CSR Organisational Evolutions -- CSR Frameworks -- CSR Innovation -- CSR Lifecycle -- CSR Measurement -- CSR: Australian Standard AS8003 [world first] -- CSRwire -- Cultural differences in values/ethics and decision-making -- Culture and Organization Performance -- Cultures, businesses, and global CSR -- Customer value creation -- Dame Anita Roddick -- Data protection -- David Henderson -- Decent work -- Definitions of social responsibility -- Deming 14 points model -- Demographic change -- Design for Environment (sep entry Hannover Principles) -- Development -- Dialogue -- Director Competencies and Skills -- Director Interlocks -- Director Role Position Description -- Disability -- Disclosure (CSR reporting) -- Discrimination -- Distributive Justice -- Diversity -- Dividend -- Dow Jones Sustainability Index -- Downsizing -- Due Diligence -- Duties of employees (comlpy with contract, comply with law, respect employers property) -- E-Waste -- Earth Summitt (separate entry on Rio declaration and on Agenda 21) -- Earthscan (publisher) -- EC Non-Discrimination Law -- Eco-Efficiency -- Eco-innovation -- Ecolabel -- Ecological economics -- Ecological footprint -- Ecology (separate entries on human and industrial) -- Econology -- Economic Globalization -- Economic sociology -- Economic Sociology of the CSR Movement -- Ecopreneurship -- Ecosystem -- Ecotoxity -- Education -- Elkington, John -- Embedded CSR -- Emissions trading -- Employability -- Employee participation/'ownership -- Employee Surveillance -- Employee volunteer programmes -- Employers' Forum on Age -- Employers' Forum on Disability -- EMS -- Endemic -- Energy Biofuels -- Energy—renewable -- Energy-solar -- Engagement/Stakeholder Engagement -- Enlightened Self-Interest -- Enron -- Environmental Accounting -- Environmental Audit -- Environmental ethics -- Environmental governance -- Environmental impact assessment -- Environmental law -- Environmental Management System -- Environmental protection agencies (all countries) -- Environmental Report Verification -- Environmental sustainability index (World economic forum) -- Environmental, Social and Governance Factors in Investment -- Environmental, Social and Governance Risk -- Environmentally Sensitive Accounting -- Equal Opportunity -- Equal Pay -- Equator Principles -- Ethic of responsibility to other stakeholders -- Ethical absolutism v. ethical relativism -- Ethical Corporation -- Ethical CSR -- Ethical Egoism & CSR -- Ethical problems in financial markets -- Ethical Theories -- Ethical Trading Initiative -- European Corporate Governance Institute -- European Multistakeholder Forum -- European Union Directive - The 8th Company Law Directive on Disclosure & Transparency -- European Urban Charter 1992 and 1998 -- Evolution of Corporate Governance Reports in the UK and Ireland -- Executive remuneration and CSR -- Externalities -- Externally Driven Business Case (EDBC) -- Extractive Industries Transparency Initiative (EITI) -- Exxon Mobil -- Exxon Valdez -- Factor 4 / Factor 10 -- Fair Pensions -- Fair Trade -- Fair wages -- Family Business and Corporate Social Responsibility -- Fiduciary duty -- Filial Piety & CSR -- Financial Regulations -- Financial Reporting Council (UK) -- Five Capitals Framework (Forum for the Future) -- Forest Stewardship Council -- Fortune at the bottom of the pyramid (Prahalad) -- Franchising Fraud prevention, detection and auditing -- Free range/cage-free/crate free/ethically raised -- Freedom of conscience -- Freedom of speech -- Friedman, Milton -- FTSE4Good Index -- G20.…”

Tabla de Contenidos: “…Selecting algorithms based on function -- Using Spark to generate real-time big data analytics -- Chapter 4 Math, Probability, and Statistical Modeling -- Exploring Probability and Inferential Statistics -- Probability distributions -- Conditional probability with Naïve Bayes -- Quantifying Correlation -- Calculating correlation with Pearson's r -- Ranking variable-pairs using Spearman's rank correlation -- Reducing Data Dimensionality with Linear Algebra -- Decomposing data to reduce dimensionality -- Reducing dimensionality with factor analysis -- Decreasing dimensionality and removing outliers with PCA -- Modeling Decisions with Multiple Criteria Decision-Making -- Turning to traditional MCDM -- Focusing on fuzzy MCDM -- Introducing Regression Methods -- Linear regression -- Logistic regression -- Ordinary least squares (OLS) regression methods -- Detecting Outliers -- Analyzing extreme values -- Detecting outliers with univariate analysis -- Detecting outliers with multivariate analysis -- Introducing Time Series Analysis -- Identifying patterns in time series -- Modeling univariate time series data -- Chapter 5 Grouping Your Way into Accurate Predictions -- Starting with Clustering Basics -- Getting to know clustering algorithms -- Examining clustering similarity metrics -- Identifying Clusters in Your Data -- Clustering with the k-means algorithm -- Estimating clusters with kernel density estimation (KDE) -- Clustering with hierarchical algorithms -- Dabbling in the DBScan neighborhood -- Categorizing Data with Decision Tree and Random Forest Algorithms -- Drawing a Line between Clustering and Classification -- Introducing instance-based learning classifiers -- Getting to know classification algorithms -- Making Sense of Data with Nearest Neighbor Analysis -- Classifying Data with Average Nearest Neighbor Algorithms…”

Tabla de Contenidos: “…. -- Implementing random forest regression -- Getting ready -- How to do it…”

Tabla de Contenidos: “…Real-life applications for labeling audio data -- Audio data fundamentals -- Hands-on with analyzing audio data -- Example code for loading and analyzing sample audio file -- Best practices for audio format conversion -- Example code for audio data cleaning -- Extracting properties from audio data -- Tempo -- Chroma features -- Mel-frequency cepstral coefficients (MFCCs) -- Zero-crossing rate -- Spectral contrast -- Considerations for extracting properties -- Visualizing audio data with matplotlib and Librosa -- Waveform visualization -- Loudness visualization -- Spectrogram visualization -- Mel spectrogram visualization -- Considerations for visualizations -- Ethical implications of audio data -- Recent advances in audio data analysis -- Troubleshooting common issues during data analysis -- Troubleshooting common installation issues for audio libraries -- Summary -- Chapter 11: Labeling Audio Data -- Technical requirements -- Downloading FFmpeg -- Azure Machine Learning -- Real-time voice classification with Random Forest -- Transcribing audio using the OpenAI Whisper model -- Step 1 - importing the Whisper model -- Step 2 - loading the base Whisper model -- Step 3 - setting up FFmpeg -- Step 4 - transcribing the YouTube audio using the Whisper model -- Classifying a transcription using Hugging Face transformers -- Hands-on - labeling audio data using a CNN -- Exploring audio data augmentation -- Introducing Azure Cognitive Services - the speech service -- Creating an Azure Speech service -- Speech to text -- Speech translation -- Summary -- Chapter 12: Hands-On Exploring Data Labeling Tools -- Technical requirements -- Azure Machine Learning data labeling -- Label Studio -- pyOpenAnnotate -- Data labeling using Azure Machine Learning -- Benefits of data labeling with Azure Machine Learning -- Data labeling steps using Azure Machine Learning…”