Mastering Deeplearning4jのトレーニングコース

コースのコード

dl4j

期間

21 時間 休憩を含む通常に 3 日間がかかります

要求

Knowledge in the following:

  • Java

概要

Deeplearning4jは、 JavaおよびScala用に書かれた最初の商用グレードのオープンソースの分散型ディープラーニングライブラリです。 HadoopおよびSparkと統合されたDL4Jは、分散GPUおよびCPU上のビジネス環境で使用されるように設計されています。

観客

このコースは、プロジェクトでDeeplearning4jを利用しようとしているエンジニアおよび開発者を対象としています。

このコースの後、参加者は以下のことができるようになります。

Machine Translated

コース概要

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

お客様の声

★★★★★
★★★★★

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