Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala.
|dl4j||Mastering Deeplearning4j||21小时||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|
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