Informed by psychology and neuroscience, Cavanagh argues that in order to capture students' attention, harness their working memory, bolster their long-term retention, and enhance their motivation, educators should consider the emotional impact of their teaching style and course design.

Informed by psychology and neuroscience, Cavanagh argues that in order to capture students' attention, harness their working memory, bolster their long-term retention, and enhance their motivation, educators should consider the emotional impact of their teaching style and course design.

Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow

At the crossroads between The Shallows and Presence, Hivemind is a provocative look at how communities can sync up around shared ideas, and how this hive mentality is contributing to today's polarized times. Hivemind: A collective consciousness in which we share consensus thoughts, emotions, and opinions; a phenomenon whereby a group of people function as if with a single mind. Our views of the world are shaped by the stories told by our self-selected communities. Whether seeking out groups that share our tastes, our faith, our heritage, or other interests, since the dawn of time we have taken comfort in defining ourselves through our social groups. But what happens when we only socialize with our chosen group, to the point that we lose the ability to connect to people who don't share our passions? What happens when our tribes merely confirm our world view, rather than expand it? We have always been a remarkably social species-our moods, ideas, and even our perceptions of reality synchronize without our conscious awareness. The advent of social media and smartphones has amplified these tendencies in ways that spell both promise and peril. Our hiveish natures benefit us in countless ways-combatting the mental and physical costs of loneliness, connecting us with collaborators and supporters, and exposing us to entertainment and information beyond what we can find in our literal backyards. But of course, there are also looming risks-echo chambers, political polarization, and conspiracy theories that have already begun to have deadly consequences. Leading a narrative journey from the site of the Charlottesville riots to the boardrooms of Facebook, considering such diverse topics as zombies, neuroscience, and honeybees, psychologist and emotion regulation specialist Sarah Rose Cavanagh leaves no stone unturned in her quest to understand how social technology is reshaping the way we socialize. It's not possible to turn back the clocks, and Cavanagh argues that there's no need to; instead, she presents a fully examined and thoughtful call to cut through our online tribalism, dial back our moral panic about screens and mental health, and shore up our sense of community. With compelling storytelling and shocking research, Hivemind is a must-read for anyone hoping to make sense of the dissonance around us.

Inspired by his popular TED Talk "3 Rules to Spark Learning," this book combines brain science research, proven teaching methods, and Ramsey's personal story to empower you to improve your students' learning experiences by inspiring inquiry and harnessing its benefits. If you want to engage students, this is the book for you.

Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations. What You Will Learn Be introduced to machine learning, Spark, and Spark MLlib 2.4.xAchieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM librariesDetect anomalies with the Isolation Forest algorithm for SparkUse the Spark NLP and Stanford CoreNLP libraries that support multiple languagesOptimize your ML workload with the Alluxio in-memory data accelerator for SparkUse GraphX and GraphFrames for Graph AnalysisPerform image recognition using convolutional neural networksUtilize the Keras framework and distributed deep learning libraries with Spark Who This Book Is For Data scientists and machine learning engineers who want to take their knowledge to the next level and use Spark and more powerful, next-generation algorithms and libraries beyond what is available in the standard Spark MLlib library; also serves as a primer for aspiring data scientists and engineers who need an introduction to machine learning, Spark, and Spark MLlib.