This course is aimed at the practicing data scientist who is eager to get started with deep learning, as well as software engineers and technical managers interested in a thorough, hands-on overview of deep learning and its integration with Apache Spark.
The 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.
Each topic includes lecture content along with hands-on labs in the Databricks notebook environment.
After taking this class, students will be able to:
Data scientists, analysts, architects, software engineers, and technical managers who want to learn deep learning and apply it at scale using Apache Spark.
Data scientists, analysts, architects, software engineers, and technical managers who want to learn deep learning and apply it at scale using Apache Spark.
Module | Lecture | Hands-on |
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Intro to Neural Networks with Keras I |
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Intro to Neural Networks with Keras II |
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MLflow |
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Convolutional Neural Networks |
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Transfer Learning |
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Horovod |
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