Just Enough Scala for Spark
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Talks & recordings
5 connected sessions
Getting Started with Apache MXNet and Modeling Churn Prediction
A short background on Deep Learning with an introduction to the powerful and scalable Apache MXNet framework. We dive into internals of MXNet, its programming model and operators, and build a neural network for churn prediction.
Introduction to Sparklyr
This session covers what sparklyr is, and how it can be used to analyze, visualize and perform machine learning in Spark from R. Covers installation, configuration, data wrangling with SQL or dplyr, modeling in MLlib or H2O.
Just Enough Scala for Spark
A hands-on tutorial presenting the Scala features needed to work effectively with Apache Spark. You can think of Spark as a Scala DSL, owing its success to the Scala Collections API.
PySpark Beyond Shuffling: Why It Isn't Magic and Where the Magic Actually Is
Deep dive into PySpark performance, understanding when and why shuffles happen, what optimizations are possible, and where PySpark's real magic lies for data engineers and scientists.
Word Embeddings: Past, Present and Future
Word Embeddings are both a hot research topic and a useful tool for NLP practitioners. The historical part focuses on work since the 1950s in computer science, cognitive science, and computational linguistics. The second part covers recent developments including compositionality of meaning, representing words as probability distributions, and learning representations of knowledge graphs.
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