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