Tabla de Contenidos: “…Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- 1 Introduction to Cognitive Computing -- 1.1 Introduction: Definition of Cognition, Cognitive Computing -- 1.2 Defining and Understanding Cognitive Computing -- 1.3 Cognitive Computing Evolution and Importance -- 1.4 Difference Between Cognitive Computing and Artificial Intelligence -- 1.5 The Elements of a Cognitive System -- 1.5.1 Infrastructure and Deployment Modalities -- 1.5.2 Data Access, Metadata, and Management Services -- 1.5.3 The Corpus, Taxonomies, and Data Catalogs -- 1.5.4 Data Analytics Services -- 1.5.5 Constant Machine Learning -- 1.5.6 Components of a Cognitive System -- 1.5.7 Building the Corpus -- 1.5.8 Corpus Administration Governing and Protection Factors -- 1.6 Ingesting Data Into Cognitive System -- 1.6.1 Leveraging Interior and Exterior Data Sources -- 1.6.2 Data Access and Feature Extraction -- 1.7 Analytics Services -- 1.8 Machine Learning -- 1.9 Machine Learning Process -- 1.9.1 Data Collection -- 1.9.2 Data Preparation -- 1.9.3 Choosing a Model -- 1.9.4 Training the Model -- 1.9.5 Evaluate the Model -- 1.9.6 Parameter Tuning -- 1.9.7 Make Predictions -- 1.10 Machine Learning Techniques -- 1.10.1 Supervised Learning -- 1.10.2 Unsupervised Learning -- 1.10.3 Reinforcement Learning -- 1.10.4 The Significant Challenges in Machine Learning -- 1.11 Hypothesis Space -- 1.11.1 Hypothesis Generation -- 1.11.2 Hypotheses Score -- 1.12 Developing a Cognitive Computing Application -- 1.13 Building a Health Care Application -- 1.13.1 Healthcare Ecosystem Constituents -- 1.13.2 Beginning With a Cognitive Healthcare Application -- 1.13.3 Characterize the Questions Asked by the Clients -- 1.13.4 Creating a Corpus and Ingesting the Content -- 1.13.5 Training the System…”

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: “…Lesson Review -- Lesson 13 Sharing Your Work -- Sharing to Apple Devices -- Sharing to Optical Media -- Creating a DVD -- Creating an HD Disc -- Sharing to the Web -- Creating a Custom Web Movie -- Exporting Audio -- Creating a Stereo Mix -- Exporting Channel Groups -- Making a Multichannel QuickTime Movie -- Exporting Audio to OMF -- Delivering Corresponding Video -- Exporting QuickTime Movies -- Creating Reference Movies -- Exporting Project Data -- Lesson Review -- Appendix A: Working with RED Footage -- Installing the Plug-in -- Managing RED Media -- Ingesting the Footage -- Editing with RED Footage -- Outputting a RED Project -- Index -- Media…”

Tabla de Contenidos: “…Chapter 8: Ensembling on Big Data -- Ensembling -- Types of ensembling -- Bagging -- Boosting -- Advantages and disadvantages of ensembling -- Random forests -- Gradient boosted trees (GBTs) -- Classification problem and dataset used -- Data exploration -- Training and testing our random forest model -- Training and testing our gradient boosted tree model -- Summary -- Chapter 9: Recommendation Systems -- Recommendation systems and their types -- Content-based recommendation systems -- Dataset -- Content-based recommender on MovieLens dataset -- Collaborative recommendation systems -- Advantages -- Disadvantages -- Alternating least square - collaborative filtering -- Summary -- Chapter 10: Clustering and Customer Segmentation on Big Data -- Clustering -- Types of clustering -- Hierarchical clustering -- K-means clustering -- Bisecting k-means clustering -- Customer segmentation -- Dataset -- Data exploration -- Clustering for customer segmentation -- Changing the clustering algorithm -- Summary -- Chapter 11: Massive Graphs on Big Data -- Refresher on graphs -- Representing graphs -- Common terminology on graphs -- Common algorithms on graphs -- Plotting graphs -- Massive graphs on big data -- Graph analytics -- GraphFrames -- Building a graph using GraphFrames -- Graph analytics on airports and their flights -- Datasets -- Graph analytics on flights data -- Summary -- Chapter 12: Real-Time Analytics on Big Data -- Real-time analytics -- Big data stack for real-time analytics -- Real-time SQL queries on big data -- Real-time data ingestion and storage -- Real-time data processing -- Real-time SQL queries using Impala -- Flight delay analysis using Impala -- Apache Kafka -- Spark Streaming -- Trending videos -- Summary -- Chapter 13: Deep Learning Using Big Data -- Introduction to neural networks -- Perceptron -- Problems with perceptrons…”

Tabla de Contenidos: “…-- Hallucinations -- Old information -- The vanilla RAG framework -- Ingestion pipeline -- Retrieval pipeline -- Generation pipeline -- What are embeddings? …”

Tabla de Contenidos: “…Deleting objects permanently using the web console -- Deleting objects permanently using gsutil -- Use case - restricting access with both ACLs and IAM -- Managing permissions in bucket using the GCP console -- Use case - signed and timed URLs -- Setting up signed URLs for cloud storage -- Use case - reacting to object changes -- Setting up object change notifications with the gsutil notification watchbucket -- Use case - using customer supplied encryption keys -- Use case - auto-syncing folders -- Use case - mounting GCS using gcsfuse -- Mounting GCS buckets -- Use case - offline ingestion options -- Summary -- Chapter 6: Relational Databases -- Relational databases, SQL, and schemas -- OLTP and the ACID properties -- Scaling up versus scaling out -- GCP Cloud SQL -- Creating a Cloud SQL instance -- Creating a database in a Cloud SQL instance -- Importing a database -- Testing Cloud SQL instances -- Use case - managing replicas -- Use case - managing certificates -- Use case - operating Cloud SQL through VM instances -- Automatic backup and restore -- Cloud Spanner -- Creating a Cloud Spanner instance -- Creating a database in Cloud Spanner instances -- Querying a database in a Cloud Spanner instance -- Interleaving tables in Cloud Spanner -- Summary -- Chapter 7: NoSQL Databases -- NoSQL databases -- Cloud Bigtable -- Fundamental properties of Bigtable -- Columnar datastore -- Denormalization -- Support for ACID properties -- Working with Bigtable -- When to use Bigtable -- Solving hot-spotting -- Choosing storage for Bigtable -- Solving performance issues -- Ideal row key choices -- Performing operations on Bigtable -- Creating and operating an HBase table using Cloud Bigtable -- Exporting/Importing a table from Cloud Bigtable -- Scaling GCP Cloud BigTable -- The Google Cloud Datastore -- Comparison with traditional databases…”

Tabla de Contenidos: “…Creating a class that has orderable objects -- Deleting from a list of complicated objects -- Chapter 9: Functional Programming Features -- Writing generator functions with the yield statement -- Applying transformations to a collection -- Using stacked generator expressions -- Picking a subset - three ways to filter -- Summarizing a collection - how to reduce -- Combining the map and reduce transformations -- Implementing ``there exists'' processing -- Creating a partial function -- Writing recursive generator functions with the yield from statement -- Chapter 10: Working with Type Matching and Annotations -- Designing with type hints -- Using the built-in type matching functions -- Using the match statement -- Handling type conversions -- Implementing more strict type checks with Pydantic -- Including run-time valid value checks -- Chapter 11: Input/Output, Physical Format, and Logical Layout -- Using pathlib to work with filenames -- Replacing a file while preserving the previous version -- Reading delimited files with the CSV module -- Using dataclasses to simplify working with CSV files -- Reading complex formats using regular expressions -- Reading JSON and YAML documents -- Reading XML documents -- Reading HTML documents -- Chapter 12: Graphics and Visualization with Jupyter Lab -- Starting a Notebook and creating cells with Python code -- Ingesting data into a notebook -- Using pyplot to create a scatter plot -- Using axes directly to create a scatter plot -- Adding details to markdown cells -- Including Unit Test Cases in a Notebook -- Chapter 13: Application Integration: Configuration -- Finding configuration files -- Using TOML for configuration files -- Using Python for configuration files -- Using a class as a namespace for configuration -- Designing scripts for composition -- Using logging for control and audit output…”

Tabla de Contenidos: “…Preface -- Part 1: Database Model: Business and Technical Design Considerations -- 1 -- Data, Databases, and Design -- Data -- Databases -- A teeny-tiny bit about the evolution of databases -- DBMS -- Database design -- Data modeling -- Database modeling -- Considerations for a good database design -- Business aspect -- Ingestion -- Technical aspect -- Choosing the right database -- Relational database -- NoSQL database -- Summary -- 2 -- Handling Data on the Cloud -- Types of cloud services -- Use case categories -- The benefits of cloud computing -- Data applications on cloud -- Storage -- Backup and disaster recovery -- Analytics and insights -- Application development -- User experience and personalization -- Managed, unmanaged, and database as a service -- Managed databases -- Unmanaged databases -- Database as a service -- Cloud database considerations -- A quick follow-up -- Summary -- Part 2: Structured Data -- 3 -- Database Modeling for Structured Data -- Structured data -- Rows and columns -- Transactional applications -- Analytical applications -- Using an RDBMS for structured data -- Atomicity -- Consistency -- Isolation -- Durability -- Considerations for your RDBMS -- Structured query language -- Sample SQL queries -- Summary -- 4 -- Setting Up a Fully Managed RDBMS -- Fully managed databases -- Fully managed RDBMS -- Cloud SQL -- Setting up and configuring a fully managed RDBMS -- Creating a Cloud SQL instance for MySQL -- Connecting to the instance -- Creating a database -- Creating a table -- Inserting values -- Querying values -- Creating an application with the Cloud database -- Configuring the Cloud Functions service account -- Creating a Cloud Function -- Operational aspects of cloud relational databases -- Migration -- Monitoring -- Query Insights -- Security -- Summary -- 5 -- Designing an Analytical Data Warehouse…”

Tabla de Contenidos: “…The Global Infrastructure Layer -- 3.3.4. Data Ingestion Layer -- 3.3.5. Data Analysis -- 3.3.6. Application Layer -- 3.3.7. …”

Tabla de Contenidos: “…-- Event streaming -- Event correlation -- Azure implementation of event processing -- Architectural components of Event Hubs -- Simple event processing -- Event stream processing -- Complex event processing -- Summary -- Chapter 2: Introducing Azure Stream Analytics and Key Advantages -- Services offered by Microsoft -- Introduction to Azure Stream Analytics -- Configuration of Azure Stream Analytics -- Key advantages of Azure Stream Analytics -- Security -- Programmer productivity -- Declarative SQL constructs -- Built-in temporal semantics -- Lowest total cost of ownership -- Mission-critical and enterprise-less scalability and availability -- Global compliance -- Microsoft Cortana Intelligence suite integration -- Azure IoT integration -- Summary -- Chapter 3: Designing Real-Time Streaming Pipelines -- Differencing stream processing and batch processing -- Logical flow of processing -- Out of order and late arrival of data -- Session grouping and windowing challenges -- Message consistency -- Fault tolerance, recovery, and storage -- Source -- Communication and collection -- Ingest, queue, and transform -- Hot path -- Cold path -- Data retention -- Presentation and action -- Canonical Azure architecture -- Summary -- Chapter 4: Developing Real-Time Event Processing with Azure Streaming -- Stream Analytics tools for Visual Studio…”

Tabla de Contenidos: “…Multi-metric jobs -- Population Jobs -- Advanced Jobs -- Create a machine learning job -- Data visualizer -- Single metric Job -- Managing jobs -- Job settings -- Job config -- Datafeed -- Counts -- JSON -- Job messages -- Datafeed preview -- Anomaly explorer -- Single metric viewer -- Multi metric job -- Explore multi metric job result -- Population job -- Summary -- Chapter 11: Create Super Cool Dashboard from a Web Application -- JDBC input plugin -- Scheduling -- Maintaining the last SQL value -- Fetch size -- Configuring Logstash for database input -- Creating a dashboard using MySQL data -- Creating visualizations -- Total blog and top blog count -- Blogger-wise blog counts -- Tag cloud for blog categories -- Blogger name-category-views-blog pie chart -- Tabular view of blog details -- Create dashboard -- Summary -- Chapter 12: Different Use Cases of Kibana -- Time-series data handling -- Conditional formatting -- Tracking trends -- A visual builder for handling time series data -- GeoIP for Elastic Stack -- Ingest node -- GeoIP with Packetbeat data -- Summary -- Chapter 13: Creating Monitoring Dashboards Using Beats -- Configuring the Beats -- Filebeat -- Configuring Filebeat -- Metricbeat -- Configuring Metricbeat -- Enabling the modules using the metricbeat.yml file -- Enabling the modules from the modules.d directory -- Packetbeat -- Configuring Packetbeat -- Creating visualizations using Beat data -- Visualization using Filebeat -- Visualization using Metricbeat -- Visualization using Packetbeat -- Creating the dashboard -- Importing Beat dashboards -- Importing dashboards in Filebeat -- Importing dashboards in Metricbeat -- Importing dashboards in Packetbeat -- Summary -- Chapter 14: Best Practices -- Requirement of test environment -- Picking the right time filter field -- Avoiding large document indexing -- Avoiding sparsity…”

Tabla de Contenidos: “…-- Version prefixing -- Virtual hosts -- Serving static content -- Serving static content from Sanic -- Serving static content with Nginx -- Streaming static content -- Summary -- Chapter 4: Ingesting HTTP Data -- Technical requirements -- Reading cookies and headers -- Headers are flexible -- Authentication headers -- Context headers -- Sanic extracts header data for us…”

Tabla de Contenidos: “…-- Problem statement -- Reference architecture for a data-intensive system -- Component view -- Data ingest -- Data preparation -- Data processing -- Workflow management -- Data access -- Data insight -- Data governance -- Data pipeline -- Oracle's information management conceptual reference architecture -- Conceptual view -- Oracle's information management reference architecture -- Data process view -- Reference architecture - business view -- Real-life use case examples -- Machine learning use case -- Data enrichment use case -- Extract transform load use case -- Desired properties of a data-intensive system -- Defining architectural principles -- Principle 1 -- Principle 2 -- Principle 3 -- Principle 4 -- Principle 5 -- Principle 6 -- Principle 7 -- Listing architectural assumptions -- Architectural capabilities -- UI capabilities -- Content mashup -- Multi-channel support -- User workflow -- AR/VR support -- Service gateway/API gateway capabilities -- Security -- Traffic control -- Mediation -- Caching -- Routing -- Service orchestration -- Business service capabilities -- Microservices -- Messaging…”

Tabla de Contenidos: “…Storing and transforming real-time data using Kinesis Data Firehose -- Different ways of ingesting data from on-premises into AWS -- AWS Storage Gateway -- Snowball, Snowball Edge, and Snowmobile -- AWS DataSync -- AWS Database Migration Service -- Processing stored data on AWS -- AWS EMR -- AWS Batch -- Summary -- Exam Readiness Drill - Chapter Review Questions -- Chapter 4: Data Preparation and Transformation -- Identifying types of features -- Dealing with categorical features -- Transforming nominal features -- Applying binary encoding -- Transforming ordinal features -- Avoiding confusion in our train and test datasets -- Dealing with numerical features -- Data normalization -- Data standardization -- Applying binning and discretization -- Applying other types of numerical transformations -- Understanding data distributions -- Handling missing values -- Dealing with outliers -- Dealing with unbalanced datasets -- Dealing with text data -- Bag of words -- TF-IDF -- Word embedding -- Summary -- Exam Readiness Drill - Chapter Review Questions -- Chapter 5: Data Understanding and Visualization -- Visualizing relationships in your data -- Visualizing comparisons in your data -- Visualizing distributions in your data -- Visualizing compositions in your data -- Building key performance indicators -- Introducing QuickSight -- Summary -- Exam Readiness Drill - Chapter Review Questions -- Chapter 6: Applying Machine Learning Algorithms -- Introducing this chapter -- Storing the training data -- A word about ensemble models -- Supervised learning -- Working with regression models -- Introducing regression algorithms -- Least squares method -- Creating a linear regression model from scratch -- Interpreting regression models -- Checking adjusted R squared -- Regression modeling on AWS -- Working with classification models -- Forecasting models…”

Tabla de Contenidos: “…-- Loss functions -- Gradient descent steps -- The vanishing gradient problem -- Using optimizers -- Optimization algorithms -- Network tuning -- Understanding embeddings -- Word embeddings -- Training embeddings -- Listing common network architectures -- Common networks -- Tools and packages -- Introducing GenAI and LLMs -- Unveiling language models -- Transformers and self-attention -- Transfer Learning -- GPT in action -- Summary -- Chapter 12: Implementing Machine Learning Solutions with MLOps -- Introducing MLOps -- A model pipeline overview -- Understanding data ingestion…”

Tabla de Contenidos: “…Hormonal Control of Aggressive Behavior -- Impulse Control -- Role of the vmPFC -- Brain Development and Impulse Control -- Serotonin and Impulse Control -- Moral Decision Making -- Communication of Emotions -- Facial Expression of Emotions: Innate Responses -- Neural Basis of the Communication of Emotions: Recognition -- Neural Basis of the Communication of Emotions: Expression -- Feeling Emotions -- The James-Lange Theory -- Feedback from Emotional Expressions -- Chapter 12. Ingestive Behavior -- Drinking -- Physiological Regulatory Mechanisms -- Two Types of Thirst -- Neural Mechanisms of Thirst -- What Is Metabolism? …”

Tabla de Contenidos: “…. -- There's more... -- Ingesting data from Kafka to Storm -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Pushing data from Kafka to Elastic -- Getting ready -- How to do it... -- How it works... -- See also -- Inserting data from Kafka to SolrCloud -- Getting ready -- How to do it... -- How it works... -- See also -- Building a Kafka producer with Akka -- Getting ready -- How to do it... -- How it works... -- There's more... -- Building a Kafka consumer with Akka -- Getting ready -- How to do it... -- Storing data in Cassandra -- Getting ready -- How to do it... -- How it works... -- Running Kafka on Mesos -- Getting ready -- How to do it... -- How it works... -- There's more... -- Reading Kafka with Apache Beam -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Writing to Kafka from Apache Beam -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Index…”

Tabla de Contenidos: “…-- Fundamentals -- Data life cycle -- Common challenges and solutions -- Introduction to exploring data in BigQuery -- Exploring data in the BigQuery SQL workspace -- Exploring schema and table structure -- Exploring data using SQL -- Exploring data using the bq command-line interface -- Exploring data with visualization tools -- Enhancing data exploration in BigQuery -- Advanced approaches -- Best practices -- Summary -- Chapter 4: Loading and Transforming Data -- Technical requirements -- Exploring data loading techniques -- Batch loading data -- Streaming ingestion of data -- Scheduled loading of data…”

Tabla de Contenidos: “…-- The browser or the timeline -- Initial assembly or rough cut -- Ingesting the media -- Setting up media folders in the Finder -- Importing media directly -- Logging -- Categorizing the clips -- Grading the clips -- What are projects? …”

Tabla de Contenidos: “…System Objects -- Custom Objects -- Packt Gear B2C Commerce data model -- B2C Commerce APIs and integrations -- Feed-based integration support -- Job framework -- Account Manager -- Open Commerce API (OCAPI) -- Salesforce Commerce API (SCAPI) -- Integration points -- B2C Commerce quotas and governance -- API and Object quotas overview -- Key solution design quotas -- B2C Commerce Partner Marketplace -- Commerce Cloud product family -- Order Management -- Omnichannel Inventory -- Salesforce Commerce -- Commerce Marketplace -- Loyalty Management -- Summary -- Questions -- Chapter 4: Engaging Customers with Marketing Cloud -- Marketing Cloud components -- Marketing Cloud Engagement components -- Marketing Cloud Intelligence -- Marketing Cloud Personalization -- Marketing Cloud Account Engagement -- Marketing Cloud Engagement capabilities -- Email management -- Journey orchestration -- CloudPages -- Programmatic customization -- Marketing Cloud Engagement data model -- Lists -- Data extensions -- Data Designer -- Business units (BUs) -- Suppression -- Segmentation -- Marketing Cloud Engagement APIs and integrations -- Feed file-based integrations -- API integrations -- Productized integration -- Importing data into a data extension -- Marketing Cloud Engagement design considerations -- Marketing Cloud Engagement edition constraints -- Data import volumes -- Putting it all together -- Summary -- Further Reading -- Questions -- Chapter 5: Know Your Customer with Data Cloud (DC) -- Data Cloud key terms and definitions -- Data Cloud data model -- Data object and field types -- Data modeling and data mapping -- Key data model object mappings for B2C solutions -- Data Cloud capabilities -- Data Cloud editions -- Data ingestion -- Data transformation and harmonization -- Identity resolution -- Segmentation and activation -- Data insights and reporting…”