[Online] Deep Learning: Building Neural Networks Using Deeplearning4j

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Virtual Classroom: Online Delivery

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Introduction to Deep Learning Building Neural Networks Using Deeplearning4j

This class is an essential course for Data Scientists and Machine Learning Engineers looking to use Neural Networks in Production. Deep Learning: Building Neural Networks Using Deeplearning4J provides participants with the information they need to determine which type of Neural Network is best suited for the task and how to configure, train, evaluate and deploy the Neural Network using Java.

Course duration is 3 days and includes hands on Labs as well as Lecture.


  • Access to the Skymind Academy Alumni Network. You will receive exclusive access to a private Gitter channel in which you can ask technical questions and interact with other workshop attendees.
  • A copy of Deep Learning: A Practitioner's Approach
  • Skymind/Deeplearning4j Teeshirt and Sticker
  • Certificate of completion

Who Should Attend?

This class is suited for Data Scientist and Data Analysts wishing to take advantage of recent breakthroughs in the field of Deep Learning.

Course Prerequisites

Attendees with some programming experience will benefit the most from this course. The labs and Neural Networks will be built using Java using DeepLearning4J and IntelliJ but Python coders and SQL users are welcome.

When is this training taking place?

This class will run over the following day(s):

Tuesday, October 17, 2017, 9:00 AM - 4:00 PM
Wednesday, October 18, 2017, 9:00 AM - 4:00 PM
Thursday, October 19, 2017, 9:00 AM - 4:00 PM

All times are based on Eastern Daylight Time.

What happens when I register?

Once you register, we will send you a confirmation email that includes the information you will need to attend this training.

Class Agenda

This three-day hands-on class will cover the topics listed below.

  • Introduction to DeepLearning
    • Machine Learning vs DeepLearning
  • Neural Network Basics
  • Neural Network Demo
  • Lab 1: Simplest Neural Network Lab
  • The DeepLearning4J Training UI
  • Neural Network Internals
  • Tuning a Neural Network
  • Types of Neural Networks
    • Feed Forward Neural Networks
    • Convolutional Neural Networks
    • Recurrent Neural Networks
  • DL4J Internals
    • DataVec for ETL
    • ND4J for tensor processing
    • DeepLearning4J for building and configuring Neural Networks
    • ND4J backends
  • Feed Forward Neural Networks
    • Uses of Feed Forward Neural Networks
    • LAB: building a FeedForward Neural Network
  • Ingesting Text Data
  • Recurrent Neural Networks
    • Uses of Recurrent Neural Networks
    • Lab: generating weather forecasts using a RNN
    • Lab: Classifying Sequence Data
  • Convolutional Neural Networks
    • Uses of Convolutional Neural Networks
    • Lab: Convolutional Neural Network for image classification
  • Deep Learning in Production
    • Paths to Production
    • Using GPU's
    • Distributed Training
    • Saving and Loading Trained Models
    • Early Stopping


Tom Hanlon currently leads training and workshops at Skymind. He has been an instructor in groundbreaking technologies since 2001. When the LAMP revolution transformed web development, Tom was on the front lines teaching MySQL. When the big data revolution transformed the way data was handled and stored, Tom was at Cloudera and Hortonworks teaching Hadoop to Fortune 500 data engineering teams. He has trained teams at Blizzard, Bloomberg and more.

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Virtual Classroom: Online Delivery

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