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Hands on Deep Learning with Keras, Tensorflow, and Apache Spark™

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Overview

This 1-day course is aimed at the practitioning 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, transfer learning, 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.

Learning Objectives

After taking this class, students will be able to:

  • Build a neural network with Keras and Tensorflow
  • Use transfer learning to tune models
  • Build distributed Tensorflow models with Horovod
  • Apply models at scale with Deep Learning Pipelines

Topics

  • Intro to Neural Networks with Keras
    • Activation functions
    • Evaluation metrics
    • Batch sizes, epochs, etc.
  • Convolutional Neural Networks
    • Convolutions
    • Batch Normalization
    • Max Pooling
    • ImageNet Architectures
  • Transfer Learning
    • Deep Learning Pipelines
    • Fine tuning models
  • Tensorflow
    • Estimator API
  • Horovod
    • Distributed Tensorflow training

Prerequisites:

  • Python (numpy and pandas)
  • Background in data science very helpful (recommend)
  • Basic knowledge of Spark DataFrames

Details

  • Duration: 1 Day

Target Audience

Engineers, data scientists, team leads, or managers who want to quickly gain an intuition and a practical understanding of recent trends in deep learning, including which problems are suited to deep learning and standard deep learning approaches to these problems.

Lab Requirements

  • Chrome or Firefox browser. Internet Explorer, Edge, and Safari are not supported.
  • Internet (web access)