Mastering Deeplearning4j

Getting Started

• Quickstart: Running Examples and DL4J in Your Projects
• Comprehensive Setup Guide

Introduction to Neural Networks

• Restricted Boltzmann Machines
• Convolutional Nets (ConvNets)
• Long Short-Term Memory Units (LSTMs)
• Denoising Autoencoders
• Recurrent Nets and LSTMs

Multilayer Neural Nets

• Deep-Belief Network
• Deep AutoEncoder
• Stacked Denoising Autoencoders

Tutorials

• Using Recurrent Nets in DL4J
• MNIST DBN Tutorial
• Iris Flower Tutorial
• Canova: Vectorization Lib for ML Tools
• Neural Net Updaters: SGD, Adam, Adagrad, Adadelta, RMSProp

Datasets

• Datasets and Machine Learning
• Custom Datasets
• CSV Data Uploads

Scaleout

• Iterative Reduce Defined
• Multiprocessor / Clustering
• Running Worker Nodes

Text

• DL4J's NLP Framework
• Word2vec for Java and Scala
• Textual Analysis and DL
• Bag of Words
• Sentence and Document Segmentation
• Tokenization
• Vocab Cache

Advanced DL2J

• Build Locally From Master
• Contribute to DL4J (Developer Guide)
• Choose a Neural Net
• 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