Zoltan is CTO at Datapao, helping companies build data analytics infrastructures and teaching their teams to do the same. He is also a Principal Instructor on the Databricks training team and a professor at the Central European University. Earlier he worked on RapidMiner's Spark integration and managed a petabyte-scale data infrastructure at Prezi.com.
This course offers a thorough, hands-on overview of deep learning and its integration with Apache Spark.
This course covers the fundamentals of neural networks and how to build distributed TensorFlow models on top of Spark DataFrames. Throughout the class, you will use Keras, TensorFlow, Deep Learning Pipelines, and Horovod to build and tune models. This course is taught entirely in Python.
Objectives
Upon completion, students will be able to:
Build a neural network with Keras
Explain the difference between various activation functions and optimizers
Track experiments with MLflow
Apply models at scale with Deep Learning Pipelines
Perform transfer learningBuild distributed models with Horovod
Audience
Primarily directed towards the practicing data scientist who is eager to get started with deep learning and its integration with Apache Spark
Prerequisites
Python (numpy and pandas)
Apache Spark™ for Machine Learning and Data Science or equivalent experience