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Course Outline
Getting Started
- Quickstart: Running Examples and DL4J in Your Projects
- Comprehensive Setup Guide
Convolutional Neural Networks
- Convolutional Net Introduction
- Images Are 4-D Tensors?
- ConvNet Definition
- How Convolutional Nets Work
- Maxpooling/Downsampling
- DL4J Code Sample
- Other Resources
Datasets
- Datasets and Machine Learning
- Custom Datasets
- CSV Data Uploads
Scaleout
- Iterative Reduce Defined
- Multiprocessor / Clustering
- Running Worker Nodes
Advanced DL2J
- Build Locally From Master
- Use the Maven Build Tool
- Vectorize Data With Canova
- Build a Data Pipeline
- Run Benchmarks
- Configure DL4J in Ivy, Gradle, SBT etc
- Find a DL4J Class or Method
- Save and Load Models
- Interpret Neural Net Output
- Visualize Data with t-SNE
- Swap CPUs for GPUs
- Customize an Image Pipeline
- Perform Regression With Neural Nets
- Troubleshoot Training & Select Network Hyperparameters
- Visualize, Monitor and Debug Network Learning
- Speed Up Spark With Native Binaries
- Build a Recommendation Engine With DL4J
- Use Recurrent Networks in DL4J
- Build Complex Network Architectures with Computation Graph
- Train Networks using Early Stopping
- Download Snapshots With Maven
- Customize a Loss Function
Requirements
- Java
21 Hours
Testimonials (4)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
Very flexible
Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning
I was benefit from the passion to teach and focusing on making thing sensible.
