Tabla de Contenidos: “…-- How we crush creativity early on -- Reigniting and harnessing the creative spark -- Why are bad ideas still good ideas? -- Green is good for creativity -- Part 3: Stories and top tips -- Sleepiness removes constraints . . . -- . . . but sometimes constraint is good -- Do the opposite to what people expect -- Innovator or fast follower? …”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- Contributors -- Chapter 1: Paralysis support system using IoT -- 1.1 Introduction -- 1.2 Literature review -- 1.3 Proposed healthcare system -- 1.4 System architecture -- 1.4.1 ESP32-NODE MCU -- 1.4.2 Gyro and accelerometer sensor -- 1.4.3 RF module -- 1.4.4 Encoder and decoder -- 1.4.5 LCD display -- 1.4.6 Arduino IDE -- 1.5 Results and discussion -- 1.6 Conclusion and future scope -- References -- Chapter 2: Blockchain and its applications: A review -- 2.1 Introduction -- 2.2 Blockchain architecture -- 2.3 Working of blockchain -- 2.4 Application -- 2.4.1 Healthcare -- 2.4.2 Education -- 2.4.3 Insurance -- 2.4.4 E-Commerce -- 2.4.5 Transportation -- 2.4.6 Industry of strength -- 2.5 Conclusion -- References -- Chapter 3: Data analytics tools for smart cities and smart towns -- 3.1 Introduction -- 3.2 The concept of big data analytics -- 3.3 The concept of smart cities -- 3.3.1 Data storage in smart cities -- 3.4 Need for data analytics in smart cities -- 3.4.1 Reliability -- 3.4.2 Transport -- 3.4.3 Planning -- 3.4.4 Future proofing -- 3.4.5 Effective spending -- 3.4.6 Sustainability -- 3.5 Data analytics tools -- 3.6 Data analytics tools for smart cities and towns -- 3.6.1 Hadoop -- 3.6.2 Map Reduce -- 3.6.3 Apache storm -- 3.6.4 Apache spark -- 3.6.5 Apache Flink -- 3.6.6 Flume -- 3.7 Conclusion -- References -- Chapter 4: Industrial Internet of Things and its applications in Industry 4.0 through sensor integration for a process parameter monitor and control -- 4.1 Introduction -- 4.2 Problem statement -- 4.3 Objective -- 4.4 Related works -- 4.4.1 Inferences through the survey -- 4.5 Existing methodology -- 4.5.1 Level process station -- 4.5.2 Monitoring and control of level process -- 4.5.3 SCADA…”

Tabla de Contenidos: “…-- 11.5 Women in STEM as Drivers of Innovation -- About the Author -- References -- Chapter 12: Safety Boots to Break Glass CeilingsTM -- 12.1 Passions Collide -- 12.2 The Spark -- 12.3 A Sign -- 12.4 The Groundwork -- 12.5 The Design -- 12.6 Manufacturing -- 12.7 The Launch -- 12.8 Loyalty -- 12.9 Hazard Girls -- 12.10 Holding Our Breath -- 12.11 Customer Stories -- 12.12 Gratitude -- 12.13 Conclusion -- About the Author -- Note -- Chapter 13: Intersectionality: How to Transform Headwinds to Tailwinds -- 13.1 What Is Intersectionality?…”

“…Programming skills in SQL, Spark, and Python are beneficial but not mandatory. …”

“…Video tutorials in this Learning Path will show you how to use Python for distributed task processing, perform large-scale data processing in Spark using the PySpark API, and tackle machine learning tasks with Python."…”

Tabla de Contenidos:

“…Discover the capabilities of PySpark and its application in the realm of data science. …”

“…Patrick then demonstrates that with the addition of Spark and Kafka, you can maintain a highly distributed, fault-tolerant, and scaling solution. …”

“…The 23 tutorials included in the compilation cover big data topics such as a review of Apache Spark 2.0 core concepts; an exploration of stream processing from the basics through Apache Beam; a practical look at how to do scalable, end-to-end data science in R on single machines and on Spark clusters; overviews of how to get started in Tensor Flow, architect a data platform, Scala and Spark, build data applications in AWS, build a data pipeline with Kafka, secure your Hadoop clusters; and how to visualize large, complex datasets with R, Hadoop, and Spark. …”

“…This video teaches you how to deploy machine learning models behind a REST API—to serve low latency requests from applications—without using a Spark cluster. In the process, you'll learn how to export models trained in SparkML; how to work with Docker, a convenient way to build, deploy, and ship application code for microservices; and how a model scoring service should support single on-demand predictions and bulk predictions. …”

Accés Lliure

Tabla de Contenidos: “…8.4.1 Internet Infrastructure -- 8.4.2 High Hardware and Software Cost -- 8.4.3 Less Qualified Workforce -- 8.5 Conclusion and Discussion -- References -- Chapter 9 Application of High-Performance Computing in Synchrophasor Data Management and Analysis for Power Grids -- 9.1 Introduction -- 9.2 Applications of Synchrophasor Data -- 9.2.1 Voltage Stability Analysis -- 9.2.2 Transient Stability -- 9.2.3 Out of Step Splitting Protection -- 9.2.4 Multiple Event Detection -- 9.2.5 State Estimation -- 9.2.6 Fault Detection -- 9.2.7 Loss of Main (LOM) Detection -- 9.2.8 Topology Update Detection -- 9.2.9 Oscillation Detection -- 9.3 Utility Big Data Issues Related to PMU-Driven Applications -- 9.3.1 Heterogeneous Measurement Integration -- 9.3.2 Variety and Interoperability -- 9.3.3 Volume and Velocity -- 9.3.4 Data Quality and Security -- 9.3.5 Utilization and Analytics -- 9.3.6 Visualization of Data -- 9.4 Big Data Analytics Platforms for PMU Data Processing -- 9.4.1 Hadoop -- 9.4.2 Apache Spark -- 9.4.3 Apache HBase -- 9.4.4 Apache Storm -- 9.4.5 Cloud-Based Platforms -- 9.5 Conclusions -- References -- Chapter 10 Intelligent Enterprise-Level Big Data Analytics for Modeling and Management in Smart Internet of Roads -- 10.1 Introduction -- 10.2 Fully Convolutional Deep Neural Network for Autonomous Vehicle Identification -- 10.2.1 Detection of the Bounding Box of the License Plate -- 10.2.2 Segmentation Objective -- 10.2.3 Spatial Invariances -- 10.2.4 Model Framework -- 10.2.4.1 Increasing the Layer of Transformation -- 10.2.4.2 Data Format of Sample Images -- 10.2.4.3 Applying Batch Normalization -- 10.2.4.4 Network Architecture -- 10.2.5 Role of Data -- 10.2.6 Synthesizing Samples -- 10.2.7 Invariances -- 10.2.8 Reducing Number of Features -- 10.2.9 Choosing Number of Classes -- 10.3 Experimental Setup and Results -- 10.3.1 Sparse Softmax Loss…”

“…He conducts seminars on distributed processing using Spark, real-time streaming and analytics, and best practices for ETL and data governance. …”

“…Viewers will learn: Benefits of deep learning over other machine learning techniques Advances in the field Elements of deep learning workflows, including how to address common challenges How to handle large-scale distributed training of neural networks, using TensorFlow A scalable implementation of deep neural networks for Spark How to use SparkNet to construct deep networks using existing libraries…”

“…This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, and Weights & Biases…”