Deeplearning4j Training in Cambridge

Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala.

Cambridge

Cambridge
Compass House Vision Park, Chivers Way, Histon
Cambridge, CAM CB24 9AD
United Kingdom
Cambridgeshire GB
Cambridge
Vision Park is within easy reach of Cambridge city centre and has the advantage of excellent links with the M11 and A14. Trains from London Kings Cross arrive...Read more

Deeplearning4j Course Events - Cambridge

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
dl4j Mastering Deeplearning4j Cambridge 21 hours Tue, 2018-02-27 09:30 £3300 / £4075
dl4jir DeepLearning4J for Image Recognition Cambridge 21 hours Tue, 2018-02-27 09:30 £3300 / £4075
w2vdl4j NLP with Deeplearning4j Cambridge 14 hours Wed, 2018-02-28 09:30 £2600 / £3150
w2vdl4j NLP with Deeplearning4j Cambridge 14 hours Thu, 2018-04-19 09:30 £2600 / £3150
dl4jir DeepLearning4J for Image Recognition Cambridge 21 hours Tue, 2018-04-24 09:30 £3300 / £4075
dl4j Mastering Deeplearning4j Cambridge 21 hours Tue, 2018-05-01 09:30 £3300 / £4075
w2vdl4j NLP with Deeplearning4j Cambridge 14 hours Mon, 2018-06-11 09:30 £2600 / £3150
dl4jir DeepLearning4J for Image Recognition Cambridge 21 hours Mon, 2018-06-18 09:30 £3300 / £4075
dl4j Mastering Deeplearning4j Cambridge 21 hours Mon, 2018-06-25 09:30 £3300 / £4075
w2vdl4j NLP with Deeplearning4j Cambridge 14 hours Wed, 2018-08-01 09:30 £2600 / £3150

Course Outlines

Code Name Duration Outline
dl4j Mastering Deeplearning4j 21 hours

Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.

 

Audience

This course is directed at engineers and developers seeking to utilize Deeplearning4j in their projects.

 

After this course delegates will be able to:

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
dl4jir DeepLearning4J for Image Recognition 21 hours

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.

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

 

w2vdl4j NLP with Deeplearning4j 14 hours

Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.

Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.

Audience

This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.

Getting Started

  • DL4J Examples in a Few Easy Steps
  • Using DL4J In Your Own Projects: Configuring the POM.xml File

Word2Vec

  • Introduction
  • Neural Word Embeddings
  • Amusing Word2vec Results
  • the Code
  • Anatomy of Word2Vec
  • Setup, Load and Train
  • A Code Example
  • Troubleshooting & Tuning Word2Vec
  • Word2vec Use Cases
  • Foreign Languages
  • GloVe (Global Vectors) & Doc2Vec

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