Tabla de Contenidos: “…. -- get_the_title and get_permalink functions -- See also -- Inserting link statistics tracking code in page body using plugin filters -- Getting ready -- How to do it... -- How it works... -- See also -- Troubleshooting coding errors and printing variable content -- How to do it... -- How it works... -- There's more... -- Built-in WordPress debugging features -- See also -- Creating a new simple shortcode -- How to do it... -- How it works... -- See also -- Creating a new shortcode with parameters -- How to do it... -- How it works... -- See also -- Creating a new enclosing shortcode -- How to do it... -- How it works... -- See also -- Loading a style sheet to format plugin output -- Getting ready -- How to do it... -- How it works... -- See also -- Writing plugins using object-oriented PHP -- Getting ready -- How to do it... -- How it works... -- See also -- Chapter 3: User Settings and Administration Pages -- Introduction -- Creating default user settings on plugin initialization -- How to do it... -- How it works... -- There's more... -- Deactivation function -- See also -- Storing user settings using arrays -- Getting ready -- How to do it... -- How it works... -- See also -- Removing plugin data on deletion -- Getting ready -- How to do it... -- How it works... -- See also -- Creating an administration page menu item in the settings menu -- Getting ready -- How to do it... -- How it works... -- There's more... -- Settings hook priority to determine menu order -- See also -- Creating a multi-level administration menu…”

Tabla de Contenidos: “…Display Advertising -- Sponsorships and Affiliate Advertising -- Ad Exchanges -- Chapter 64 Email Marketing -- Chapter 65 Mobility Marketing -- Widgets, Gadgets, and Mobile Applications -- A Growing Tidal Wave of Opportunity -- How Companies Use Mobile Phones to Drive Business -- Chapter 66 Social Media: Build Your Brand and Connect with Customers -- A Shift of Power -- The Real Value of Social Media -- Social Media Principles -- Chapter 67 What We Know So Far: Surprising Statistics -- Business Results from Social Media -- Chapter 68 Measuring the Effectiveness of Social Media -- Operationalize Social Media -- Chapter 69 Social Media Networks -- LinkedIn -- Facebook -- Twitter -- Social Bookmarking Sites -- Chapter 70 The Blogosphere -- Getting Started -- Chapter 71 Social Media Strategy and Planning Guide -- Social Media Strategy -- Chapter 72 Word of Mouth: Viral Marketing and Buzz -- Stunts and Pranksters -- Historical Milestone -- Chapter 73 Public Relations -- What's New, Who Cares? …”

Tabla de Contenidos: “…Implement change and improve maintainability -- Challenge -- Updating an existing e-commerce site -- Assignment -- Knowledge check -- Summary -- Chapter 11: Data Exploration with ChatGPT -- Introduction -- Business problem -- Problem and data domain -- Dataset overview -- Feature breakdown -- Prompting strategy -- Strategy 1: Task-Actions-Guidelines (TAG) prompt strategy -- Strategy 2: Persona-Instructions-Context (PIC) prompt strategy -- Strategy 3: Learn-Improvise-Feedback-Evaluate (LIFE) prompt strategy -- Data exploration of the Amazon review dataset using the free version of ChatGPT -- Feature 1: Loading the dataset -- Feature 2: Inspecting the data -- Feature 3: Summary statistics -- Feature 4: Exploring categorical variables -- Feature 5: Rating distribution -- Feature 6: Temporal trends -- Feature 7: Review length analysis -- Feature 8: Correlation study -- Data exploration of the Amazon review dataset using ChatGPT-4o -- Assignment -- Challenge -- Summary -- Chapter 12: Building a Classification Model with ChatGPT -- Introduction -- Business problem -- Problem and data domain -- Dataset overview -- Breaking the problem down into features -- Prompting strategy -- Strategy 1: Task-Actions-Guidelines (TAG) prompt strategy -- Strategy 2: Persona-Instructions-Context (PIC) prompt strategy -- Strategy 3: Learn-Improvise-Feedback-Evaluate (LIFE) prompt strategy -- Building a sentiment analysis model to accurately classify Amazon reviews using the free version of ChatGPT -- Feature 1: Data preprocessing and feature engineering -- Feature 2: Model selection and baseline training -- Feature 3: Model evaluation and interpretation -- Feature 4: Handling imbalanced data -- Feature 5: Hyperparameter tuning -- Feature 6: Experimenting with feature representation…”

Tabla de Contenidos: “…Creating a robots.txt file -- Creating separate pages for posts -- Creating meaningful URLs (slugs) -- Adding dynamic titles -- Adding other meta tags -- Creating a sitemap -- Improving social media embeds -- Open Graph meta tags -- Using the OG article meta tags -- Summary -- Chapter 9: Implementing End-to-End Tests Using Playwright -- Technical requirements -- Setting up Playwright for end-to-end testing -- Installing Playwright -- Preparing the backend for end-to-end testing -- Writing and running end-to-end tests -- Using the VS Code extension -- Reusable test setups using fixtures -- Overview of built-in fixtures -- Writing our own fixture -- Using custom fixtures -- Viewing test reports and running in CI -- Viewing an HTML report -- Running Playwright tests in CI -- Summary -- Chapter 10: Aggregating and Visualizing Statistics Using MongoDB and Victory -- Technical requirements -- Collecting and simulating events -- Creating the event model -- Defining a service function and route to track events -- Collecting events on the frontend -- Simulating events -- Aggregating data with MongoDB -- Getting the total number of views per post -- Getting the number of daily views per post -- Calculating the average session duration -- Implementing data aggregation in the backend -- Defining aggregation service functions -- Defining the routes -- Integrating and visualizing data on the frontend using Victory -- Integrating the aggregation API -- Visualizing data using Victory -- Summary -- Chapter 11: Building a Backend with a GraphQL API -- Technical requirements -- What is GraphQL? …”

Tabla de Contenidos: “…Configuration and tuning of WBI Event Broker -- 5.1 Satisfying performance requirements -- 5.1.1 WebSphere MQ real-time transport -- 5.1.2 WebSphere MQ multicast transport -- 5.1.3 Non-persistent Messages on WebSphere MQ queues -- 5.1.4 Broker collectives -- 5.1.5 JMS message selection -- 5.1.6 Broker statistics -- 5.1.7 Message flow design -- 5.2 Satisfying reliability requirements -- 5.2.1 Persistent messages on WebSphere MQ queues -- 5.2.2 Transactional control -- 5.2.3 JMS durable subscriptions -- 5.2.4 Stream-crossing within the broker -- 5.3 Satisfying availability requirements -- 5.3.1 High availability -- 5.3.2 Cloned brokers -- 5.4 Satisfying scalability requirements -- 5.4.1 Broker collectives -- 5.4.2 Topic hierarchies -- 5.5 Satisfying security requirements -- 5.5.1 ACLs and topic-based security -- 5.5.2 Authentication for real-time connections -- 5.5.3 Authentication for connections using WebSphere MQ queues -- 5.5.4 Quality of protection for messages -- 5.6 Developing a broker architecture -- 5.6.1 Tuning for performance -- 5.6.2 Tuning for reliability -- 5.6.3 Tuning for high availability -- 5.6.4 Multiple instances and multicast -- Part 2 Rationale of WBI Event Broker -- Chapter 6. …”

Tabla de Contenidos: “…A Brief - and Instructive - History of ICE Vehicle Safety -- A Short History of Everything (Car-Safety- Related) in 2 Minutes -- Examining Safety Features (and Statistics) for Electric Vehicles -- What's NHTSA's line, anyway? …”

Tabla de Contenidos: “…Cover -- Half Title -- Title -- Copyright -- Dedication -- Table of Contents -- Preface -- INTRODUCTION -- 1.1 Introduction -- 1.2 A little bit of history -- 1.3 Information -- 1.4 Digital versus analogue -- 1.5 Conversion to digital -- 1.6 Sampling theory -- 1.7 Quantization -- 1.8 Exercises -- COMPRESSION TECHNIQUES -- 2.1 Introduction -- 2.2 Compression methods -- 2.3 Letter probabilities -- 2.4 Coding methods -- 2.5 Statistical encoding -- 2.6 Repetitive sequence suppression -- 2.7 Differential encoding -- 2.8 Transform encoding -- 2.9 Exercises -- 2.10 Letter probablity program -- HUFFMAN/LEMPEL-ZIV COMPRESSION METHODS -- 3.1 Introduction -- 3.2 Huffman coding -- 3.3 Adaptive Huffman coding -- 3.4 Lempel-Ziv coding -- 3.5 Lempel-Ziv-Welsh coding -- 3.6 Variable-length-code LZW compression -- 3.7 Disadvantages with LZ compression -- 3.8 Practical Lempel-Ziv/Huffman coding -- 3.9 Exercises -- IMAGE COMPRESSION (GIF/TIFF/ PCX) -- 4.1 Introduction -- 4.2 Comparison of the different methods -- 4.3 GIF coding -- 4.4 TIFF coding -- 4.5 GIF interlaced images -- 4.6 PCX coding -- 4.7 Exercises -- IMAGE COMPRESSION (JPEG) -- 5.1 Introduction -- 5.2 JPEG coding -- 5.3 JPEG decoding -- 5.4 JPEG file format -- 5.5 JPEG modes -- 5.6 Exercises -- VIDEO SIGNALS -- 6.1 Introduction -- 6.2 Color-difference signals -- 6.3 Quadrature modulation -- 6.4 Baseband video signals -- 6.5 Digitizing TV signals -- 6.6 100 Hz pictures -- 6.7 Compressed TV -- 6.8 HDTV quality -- 6.9 Exercise -- MOTION VIDEO COMPRESSION -- 7.1 Motion video -- 7.2 MPEG-1 overview -- 7.3 MPEG-1 video compression -- 7.4 MPEG-1 compression process -- 7.5 MPEG-1 decoder -- 7.6 MPEG-1 audio compression -- 7.7 MPEG-2 -- 7.8 MPEG-2 system layer -- 7.9 Other MPEG-2 enhancements -- 7.10 MPEG-2 bit rate -- 7.11 Exercises -- SPEECH AND AUDIO SIGNALS -- 8.1 Introduction -- 8.2 PCM parameters…”

Tabla de Contenidos: “…2.7.1 Discrete Distributions -- 2.7.1.1 Binomial Distribution -- 2.7.1.2 Poisson Distribution -- 2.7.1.3 Geometric Distribution -- 2.7.2 Continuous Distributions -- 2.7.2.1 Exponential Distribution -- 2.7.2.2 Continuous Uniform Distribution -- 2.7.2.3 Normal Distribution -- 2.7.2.4 Lognormal Distribution -- 2.7.2.5 Weibull Distribution -- 2.7.2.6 Gamma Distribution (and Chi-Squared) -- 2.7.2.7 Beta Distribution -- 2.7.3 Truncated Distributions -- 2.7.4 Multivariate Distributions -- 2.8 Exercises -- References -- Chapter 3 Elements of Component Reliability -- 3.1 Definitions for Reliability -- 3.1.1 Reliability Function -- 3.1.2 MTTF, MRL, MTBF, and Quantiles -- 3.1.3 Hazard Rate and Failure Rate -- 3.2 Common Distributions in Component Reliability -- 3.2.1 Exponential Distribution and Poisson Distribution -- 3.2.2 Weibull Distribution -- 3.2.3 Gamma Distribution -- 3.2.4 Normal Distribution -- 3.2.5 Lognormal Distribution -- 3.2.6 Beta Distribution -- 3.3 Exercises -- References -- Chapter 4 Basic Reliability Mathematics: Statistics -- 4.1 Introduction -- 4.2 Descriptive Statistics -- 4.3 Empirical Distributions and Histograms -- 4.4 Parameter Estimation: Point Estimation -- 4.4.1 Method of Moments -- 4.4.2 Linear Regression -- 4.4.3 Maximum Likelihood Estimation -- 4.4.4 Bayesian Parameter Estimation -- 4.5 Parameter Estimation: Interval Estimation -- 4.5.1 Confidence Intervals -- 4.5.2 Credible Intervals -- 4.6 Hypothesis Testing and Goodness of Fit -- 4.6.1 Hypothesis Testing Basics -- 4.6.2 Chi-Squared Test -- 4.6.3 Kolmogorov-Smirnov (K-S) Test -- 4.7 Linear Regression -- 4.8 Exercises -- References -- Chapter 5 Reliability Data Analysis and Model Selection -- 5.1 Context and Types of Data -- 5.1.1 Types of Field and Test Data -- 5.1.2 Complete Data -- 5.1.3 Censored Data -- 5.1.3.1 Left, Right, and Interval Censoring -- 5.1.3.2 Type I Censoring…”

Tabla de Contenidos: “…. -- 2.18 Exercise -- 2.19 Assessment question -- Further reading -- 3 Data theory and taxonomy of data -- Abbreviations -- 3.1 Introduction -- 3.2 Data as a whole -- 3.2.1 Structured data -- 3.2.2 Semistructured data -- 3.2.3 Unstructured data -- 3.2.4 Quantitative and qualitative data analysis -- 3.2.4.1 Qualitative data -- 3.2.4.1.1 Types of qualitative data -- 3.2.4.1.2 Importance of qualitative data -- 3.2.4.1.3 Role of qualitative data in the industry 4.0 -- 3.2.4.1.4 Main approaches to qualitative data analysis -- 3.2.4.1.5 Steps to qualitative data analysis -- 3.2.4.1.6 Qualitative data collection methods -- 3.2.4.2 Quantitative data -- 3.2.4.2.1 Types of Quantitative Data -- Discrete data -- Continuous data -- Interval data -- Trend analysis -- Conjoint analysis -- TURF analysis -- 3.2.4.2.2 Quantitative data collection methods -- Probability sampling -- Surveys/questionnaires -- Web-based questionnaire -- Mail questionnaire -- Observations -- Document review in quantitative data collection -- 3.3 Views of data -- 3.3.1 Types of statistical analysis -- 3.3.1.1 Descriptive analysis -- 3.3.1.2 Inferential analysis -- 3.3.1.3 Predictive analysis -- 3.3.1.4 Prescriptive analysis -- 3.3.1.5 Exploratory data analysis -- 3.3.1.6 Causal analysis…”

Tabla de Contenidos: “…Vibration-based diagnosis of defect embedded in inner raceway of ball bearing using 1D convolutional neural network -- 3.1 Introduction -- 3.2 2D CNN-a brief introduction -- 3.3 1D convolutional neural network -- 3.4 Statistical parameters for feature extraction -- 3.5 Dataset used -- 3.6 Results -- 3.7 Conclusion -- References -- 4. …”

Tabla de Contenidos: “…Sexism and Gender Bias in the Audio Industry "Opting out" of the conversation Sexual Harassment On motherhood On Age The Annenberg Study Recording Academy Task Force The Grammys and Recording Academy Class of 2019 Audio Engineering Society 2019 Chapter Two: Outreach: Organizations and Current Initiatives Pull Digital Audio Eco Feminism Erin Barra and Beats By Girlz (USA) Fun Facts: Ableton Live and Ableton Push Phebean Adedamola Oluwagbemi (Nigeria) Audio Girl Africa (Nigeria) Female Frequency (USA) Dani Mari (USA) I Am Snow Angel (USA) Women in Sound Zine and Madeleine Campbell (USA) Gender Amplified (USA) Ebonie Smith (USA) The Creator's Suite (USA) Abhita Austin (USA) Yorkshire Sound Women Network (England) International Women Working in Film Sound Women (United Kingdom) Terri Winston and Women's Audio Mission (USA) SoundGirls.org Chapter Three: Radio and Podcasts Careers in radio and podcasts Tools of the Trade: Radio Cathy Hughes (USA) Ann Charles (England) Ioana Barbu Caryl Owen (USA) Suraya Mohamed (USA) Lorna White (USA) Nadia Hassan-Garschagen (Germany) Podcasts Chapter Four: Sound for Television & Film Careers in Sound for Film and Television Feature Films and Documentaries Post Production Live Broadcast Tools of the Trade: Location Sound Tools of the Trade: Post Production for television and film Jan McLaughlin, CAS (USA) Judi Lee-Headman (England) Lori Dovi (USA) Ai-Ling Lee (Singapore) April Tucker (USA) Karol Urban, CAS (USA) Haniya Aslam (Pakistan) 'Lisa' Xiang Li (China) Sajida Khan (India) Catharine Wood (USA) Anna Bertmark (Sweden) Chapter Five: Music Recording and Electronic Music Careers in Music Recording and Electronic Music Electronic Music Pamela Z Suzanne Ciani Cecilia Wu (China) Producers Kay Huang (Taiwan) Elaine Martone (USA) Erica Brenner (USA) Linda Briceño (Venezuela) Virginia Read (Australia) Mastering Engineers Emily Lazar (USA) Jett Galindo (Philippines) Piper Payne (USA) Anna Frick (USA) Darcy Proper (USA) Fun facts: Vinyl Mastering Fun Facts: Loudness Wars Recording Engineers Lenise Bent (USA) Erin Tonkon (USA) Jill Zimmermann (Germany) Wendy Beines (Argentina) Other Women in Music Recording Chapter Six: Hardware and Software Design Careers in Hardware and Software Fun Facts: Make your own Audio Plugin with MATLAB Tools of the Trade: Hardware and Software Design Laurie Spiegel (USA) Carla Scaletti (USA) EveAnna Dauray Manley (USA) Marina Bosi (Italy) Erisa Sato (Japan) Elisabeth Löchen (France) Marie Louise Killick (England) Carol Bousquet (USA) Dawn Birr (USA) Other women in Hardware and Software Chapter Seven: Acoustics and Design Careers in Acoustics Tools of the Trade: Acoustics Fun Facts: Weighting Curves Jamie Angus-Whiteoak (England) Fun Facts: Spread Spectrum Elizabeth Cohen (USA) Samantha Weller (USA) Fun Facts: Ray Tracing Orla Murphy (Ireland) Fun Facts: Ear Training and Critical Listening Linda Gedemer (USA) Women in Acoustics Committee Chapter Eight: Live and Theater Sound Careers in Live Sound: Tools of the Trade: Live Sound Fun Facts: Safe Listening Levels Kathy Sander-Olhoeft (USA) Betty Cantor-Jackson (USA) Brandie Lane (USA) Karrie Keyes (USA) Michelle Sabolchick Pettinato (USA) Fela Davis Carolina Anton (Mexico) Leya Soraide (Bolivia) Tzu-Wei Peng (Taiwan) Jessica Paz (USA) Chapter Nine: Education A brief history of audio education Academic Standards Careers in Higher Education Education-related Volunteer and Non-Profit Work Tools of the Trade: Education Martha de Francisco (Columbia) Robin Coxe-Yeldham (USA) Theresa Leonard (Canada) Lisa Nigris (USA) Valeria Palomino (Mexico) Liz Dobson Cosette Collier (USA) Chapter Ten: Games, Immersive Sound, and Audio for Virtual, Augmented, and Mixed Reality Tools of the Trade: Game Sound Tools of the Trade: Immersive Audio Tools of the Trade: Binaural Audio Other tools Women in Game Sound: Industry Statistics Winifred Phillips (USA) Eiko Ishiwata (Canada) Ayako Yamauchi (Japan) Fun Facts: Ambisonics Emily Ridgeway (Australia) Fun Facts: Head Related Transfer Functions Khris Brown (USA) Fun Facts: TASCAM Portastudio Pam Aronoff (USA) Fun Facts: Wwise Anastasia Devana (Russia) Fun Facts: Virtual, Augmented, and Mixed Reality Victoria Dorn (USA) Sally Kellaway (Australia) Fei Yu (China) About the Author Glossary Recommended Reading Appendix 1 Appendix 2…”

Tabla de Contenidos: “…Networks for sequence data -- RNNs and LSTMs -- Building a better optimizer -- Gradient descent to ADAM -- Xavier initialization -- Summary -- References -- Chapter 4: Teaching Networks to Generate Digits -- The MNIST database -- Retrieving and loading the MNIST dataset in TensorFlow -- Restricted Boltzmann Machines: generating pixels with statistical mechanics -- Hopfield networks and energy equations for neural networks -- Modeling data with uncertainty with Restricted Boltzmann Machines -- Contrastive divergence: Approximating a gradient -- Stacking Restricted Boltzmann Machines to generate images: the Deep Belief Network -- Creating an RBM using the TensorFlow Keras layers API -- Creating a DBN with the Keras Model API -- Summary -- References -- Chapter 5: Painting Pictures with Neural Networks Using VAEs -- Creating separable encodings of images -- The variational objective -- The reparameterization trick -- Inverse Autoregressive Flow -- Importing CIFAR -- Creating the network from TensorFlow 2 -- Summary -- References -- Chapter 6: Image Generation with GANs -- The taxonomy of generative models -- Generative adversarial networks -- The generator model -- Training GANs -- Non-saturating generator cost -- Maximum likelihood game -- Vanilla GAN -- Improved GANs -- Deep Convolutional GAN -- Vector arithmetic -- Conditional GAN -- Wasserstein GAN -- Progressive GAN -- The overall method -- Progressive growth-smooth fade-in -- Minibatch standard deviation -- Equalized learning rate -- Pixelwise normalization -- TensorFlow Hub implementation -- Challenges -- Training instability -- Mode collapse -- Uninformative loss and evaluation metrics -- Summary -- References -- Chapter 7: Style Transfer with GANs -- Paired style transfer using pix2pix GAN -- The U-Net generator -- The Patch-GAN discriminator -- Loss -- Training pix2pix -- Use cases…”

Tabla de Contenidos: “…Existing Continuous User Authentication Techniques -- 2.4. Statistical Language Modeling -- 2.4.1. Neural Networks -- 2.4.2. …”

Tabla de Contenidos: “…Encoder network -- Decoder network -- Sequence to sequence -- Building the graph -- Training -- Inference -- TensorBoard visualization -- State-of-the-art abstractive text summarization -- Summary -- Chapter 9: Question-Answering and Chatbots Using Memory Networks -- The Question-Answering task -- Question-Answering datasets -- Memory networks for Question-Answering -- Memory network pipeline overview -- Writing a memory network in TensorFlow -- Class constructor -- Input module -- Question module -- Memory module -- Output module -- Putting it together -- Extending memory networks for dialog modeling -- Dialog datasets -- The bAbI dialog dataset -- Raw data format -- Writing a chatbot in TensorFlow -- Loading dialog datasets in the QA format -- Vectorizing the data -- Wrapping the memory network model in a chatbot class -- Class constructor -- Building a vocabulary for word embedding lookup -- Training the chatbot model -- Evaluating the chatbot on the testing set -- Interacting with the chatbot -- Putting it all together -- Example of an interactive conversation -- Literature on and related to memory networks -- Summary -- Chapter 10: Machine Translation Using the Attention-Based Model -- Overview of machine translation -- Statistical machine translation -- English to French using NLTK SMT models -- Neural machine translation -- Encoder-decoder network -- Encoder-decoder with attention -- NMT for French to English using attention -- Data preparation -- Encoder network -- Decoder network -- Sequence-to-sequence model -- Building the graph -- Training -- Inference -- TensorBoard visualization -- Summary -- Chapter 11: Speech Recognition Using DeepSpeech -- Overview of speech recognition -- Building an RNN model for speech recognition -- Audio signal representation -- LSTM model for spoken digit recognition -- TensorBoard visualization…”

Tabla de Contenidos: “…Using search algorithms in games -- Combinatorial search -- Minimax algorithm -- Alpha-Beta pruning -- Negamax algorithm -- Installing easyAI library -- Building a bot to play Last Coin Standing -- Building a bot to play Tic-Tac-Toe -- Building two bots to play Connect FourTM against each other -- Building two bots to play Hexapawn against each other -- Summary -- Chapter 10: Natural Language Processing -- Introduction and installation of packages -- Tokenizing text data -- Converting words to their base forms using stemming -- Converting words to their base forms using lemmatization -- Dividing text data into chunks -- Extracting the frequency of terms using a Bag of Words model -- Building a category predictor -- Constructing a gender identifier -- Building a sentiment analyzer -- Topic modeling using Latent Dirichlet Allocation -- Summary -- Chapter 11: Probabilistic Reasoning for Sequential Data -- Understanding sequential data -- Handling time-series data with Pandas -- Slicing time-series data -- Operating on time-series data -- Extracting statistics from time-series data -- Generating data using Hidden Markov Models -- Identifying alphabet sequences with Conditional Random Fields -- Stock market analysis -- Summary -- Chapter 12: Building A Speech Recognizer -- Working with speech signals -- Visualizing audio signals -- Transforming audio signals to the frequency domain -- Generating audio signals -- Synthesizing tones to generate music -- Extracting speech features -- Recognizing spoken words -- Summary -- Chapter 13: Object Detection and Tracking -- Installing OpenCV -- Frame differencing -- Tracking objects using colorspaces -- Object tracking using background subtraction -- Building an interactive object tracker using the CAMShift algorithm -- Optical flow based tracking -- Face detection and tracking…”

Tabla de Contenidos: “…Cover -- Table of Contents -- Introduction -- 1 Introducing LinkedIn -- Understanding the Power of LinkedIn -- Understanding the Key to Success on LinkedIn -- Understanding LinkedIn Account Types -- Exploring LinkedIn Premium Accounts -- Signing Up for LinkedIn -- Finding Your Way Around LinkedIn -- Exploring the LinkedIn Home Page -- Navigating LinkedIn -- 2 Creating Your LinkedIn Profile -- Exploring LinkedIn Profiles -- Creating a Profile That Generates Results -- Achieving Profile Completeness -- Creating a Basic Profile -- Adding Positions -- Adding Educational Information -- Adding Websites and Other Information -- Integrating Your LinkedIn Account with Twitter -- Customizing Public Profiles -- Adding Profile Summaries -- Adding Personal Information -- Specifying Contact Settings -- Adding Profile Photos -- Reordering Sections on Your LinkedIn Profile -- Viewing Your Profile -- 3 Developing Your LinkedIn Network -- Developing a Connection Strategy -- Building Your Network -- Importing Webmail Contacts -- Importing Contacts from Desktop Email Programs -- Connecting with Colleagues -- Connecting with Classmates -- Connecting with Others on LinkedIn -- Connecting with People Not on LinkedIn -- Responding to Connection Invitations -- Managing Your Connections -- Removing Connections -- Exporting Connections -- Viewing Your Network Statistics -- 4 Customizing Your LinkedIn Experience -- Customizing LinkedIn on the Account & Settings Page -- Customizing Profile Settings -- Customizing Email Notification Settings -- Customizing Home Page Settings -- Subscribing to RSS Feeds -- Subscribing to Network Updates -- Subscribing to LinkedIn Answers -- Customizing Group Invitation Settings -- Customizing Your LinkedIn Personal Information -- Customizing Your Privacy Settings -- Customizing Your LinkedIn Network Options…”

Tabla de Contenidos: “…-- Techniques for name recognition -- Lists and regular expressions -- Statistical classifiers -- Using regular expressions for NER -- Using Java's regular expressions to find entities -- Using the RegExChunker class of LingPipe -- Using NLP APIs -- Using OpenNLP for NER -- Determining the accuracy of the entity -- Using other entity types -- Processing multiple entity types -- Using the Stanford API for NER -- Using LingPipe for NER -- Using LingPipe's named entity models -- Using the ExactDictionaryChunker class -- Building a new dataset with the NER annotation tool -- Training a model -- Evaluating a model -- Summary -- Chapter 5: Detecting Part of Speech -- The tagging process -- The importance of POS taggers -- What makes POS difficult? …”

Tabla de Contenidos: “…Help Educate White Men on the Statistics -- 3. Create Spaces for White Men to Learn and Grow Together -- 4. …”

Tabla de Contenidos: “…Shrinking lists - deleting, removing, and popping -- Writing list-related type hints -- Reversing a copy of a list -- Building sets - literals, adding, comprehensions, and operators -- Shrinking sets - remove(), pop(), and difference -- Writing set-related type hints -- Chapter 5: Built-In Data Structures Part 2: Dictionaries -- Creating dictionaries - inserting and updating -- Shrinking dictionaries - the pop() method and the del statement -- Writing dictionary-related type hints -- Understanding variables, references, and assignment -- Making shallow and deep copies of objects -- Avoiding mutable default values for function parameters -- Chapter 6: User Inputs and Outputs -- Using the features of the print() function -- Using input() and getpass() for user input -- Debugging with f"{value=}" strings -- Using argparse to get command-line input -- Using invoke to get command-line input -- Using cmd to create command-line applications -- Using the OS environment settings -- Chapter 7: Basics of Classes and Objects -- Using a class to encapsulate data and processing -- Essential type hints for class definitions -- Designing classes with lots of processing -- Using typing.NamedTuple for immutable objects -- Using dataclasses for mutable objects -- Using frozen dataclasses for immutable objects -- Optimizing small objects with __slots__ -- Using more sophisticated collections -- Extending a built-in collection - a list that does statistics -- Using properties for lazy attributes -- Creating contexts and context managers -- Managing multiple contexts with multiple resources -- Chapter 8: More Advanced Class Design -- Choosing between inheritance and composition - the "is-a" question -- Separating concerns via multiple inheritance -- Leveraging Python's duck typing -- Managing global and singleton objects -- Using more complex structures - maps of lists…”

Tabla de Contenidos: “…5.1.2.10 Remote Sensing and Image Analysis -- 5.2 History of Surveillance Systems -- 5.3 Literature Review -- 5.4 Mathematical Models for Surveillance Systems -- 5.4.1 Overview of Mathematical Modeling in Surveillance -- 5.4.2 Role of Probability and Statistics in Surveillance -- 5.4.2.1 Anomaly Detection -- 5.4.2.2 Predictive Analytics -- 5.4.2.3 Risk Assessment -- 5.4.2.4 Decision Support -- 5.4.2.5 Data Fusion and Integration -- 5.4.3 Modeling Human Behavior in Surveillance Scenario -- 5.4.3.1 Behavioral Patterns -- 5.4.3.2 Machine Learning -- 5.4.3.3 Social Dynamics -- 5.4.3.4 Continuous Learning and Adaptation -- 5.4.3.5 Cognitive Modeling -- 5.4.4 Mathematical Modeling for Tracking and Motion Analysis -- 5.4.4.1 Object Tracking -- 5.4.4.2 Motion Prediction -- 5.4.4.3 Motion Analysis -- 5.4.4.4 Motion Representation -- 5.4.4.5 Trajectory Analysis -- 5.4.4.6 Data Fusion -- 5.4.4.7 Continuous Learning and Adaptation -- 5.5 Artificial Intelligence in Surveillance Systems -- 5.5.1 Object Recognition and Detection -- 5.5.2 Behavior Analysis -- 5.5.3 Facial Recognition -- 5.5.4 Video Analytics -- 5.5.5 Real-Time Alert Generation -- 5.5.6 Predictive Analytics -- 5.5.7 Data Management and Analytics -- 5.6 Use of Mathematical Models for Pre-Processing Image Data -- 5.6.1 Filtering and Smoothing -- 5.6.2 Image Enhancement -- 5.6.3 Edge Detection -- 5.6.4 Image Restoration -- 5.6.5 Feature Extraction -- 5.6.6 Dimensionality Reduction -- 5.7 Future Directions and Challenges -- 5.7.1 Deep Learning and Neural Networks -- 5.7.2 Real-Time Processing -- 5.7.3 Multi-Modal Data Fusion -- 5.7.4 Privacy-Preserving Techniques -- 5.7.5 Human-Centric Surveillance -- 5.7.6 Robustness to Adversarial Attacks -- 5.7.7 Interoperability and Scalability -- 5.7.8 Ethical and Legal Considerations -- 5.8 Conclusion -- 5.8.1 Summary of the Chapter…”