Duration
21 hours (usually 3 days including breaks)
Requirements
- Java
Overview
Deeplearning4j is an Open-Source Deep-Learning Software for Java and Scala on Hadoop and Spark.
Audience
This course is meant for engineers and developers seeking to utilize DeepLearning4J in their image recognition projects.
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