Plan Szkolenia

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

Wymagania

Knowledge in the following:

  • Java
  21 godzin
 

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