Course Information
- Quickstart: Running Examples and DL4J in Your Projects
- Comprehensive Setup Guide
- Convolutional Net Introduction
- Images Are 4-D Tensors?
- ConvNet Definition
- How Convolutional Nets Work
- Maxpooling/Downsampling
- DL4J Code Sample
- Other Resources
- Datasets and Machine Learning
- Custom Datasets
- CSV Data Uploads
- Iterative Reduce Defined
- Multiprocessor / Clustering
- Running Worker Nodes
- 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
Java
21 hours (usually 3 days including breaks)
