Overview:
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.
Pre-Requisite:
Knowledge in the following:
- Java
Course Curriculum
| Getting Started | |||
| Quickstart: Running Examples and DL4J in Your Projects Details | 00:00:00 | ||
| Comprehensive Setup Guide Details | 00:00:00 | ||
| Introduction to Neural Networks | |||
| Restricted Boltzmann Machines Details | 00:00:00 | ||
| Convolutional Nets (ConvNets) Details | 00:00:00 | ||
| Long Short-Term Memory Units (LSTMs) Details | 00:00:00 | ||
| Denoising Autoencoders Details | 00:00:00 | ||
| Recurrent Nets and LSTMs Details | 00:00:00 | ||
| Multilayer Neural Nets | |||
| Deep-Belief Network Details | 00:00:00 | ||
| Deep AutoEncoder Details | 00:00:00 | ||
| Stacked Denoising Autoencoders Details | 00:00:00 | ||
| Tutorials | |||
| Using Recurrent Nets in DL4J Details | 00:00:00 | ||
| MNIST DBN Tutorial Details | 00:00:00 | ||
| Iris Flower Tutorial Details | 00:00:00 | ||
| Canova: Vectorization Lib for ML Tools Details | 00:00:00 | ||
| Neural Net Updaters: SGD, Adam, Adagrad, Adadelta, RMSProp Details | 00:00:00 | ||
| Datasets | |||
| Datasets and Machine Learning Details | 00:00:00 | ||
| Custom Datasets Details | 00:00:00 | ||
| CSV Data Uploads Details | 00:00:00 | ||
| Scaleout | |||
| Iterative Reduce Defined Details | 00:00:00 | ||
| Multiprocessor / Clustering Details | 00:00:00 | ||
| Running Worker Nodes Details | 00:00:00 | ||
| Text | |||
| DL4J’s NLP Framework Details | 00:00:00 | ||
| Word2vec for Java and Scala Details | 00:00:00 | ||
| Textual Analysis and DL Details | 00:00:00 | ||
| Bag of Words Details | 00:00:00 | ||
| Sentence and Document Segmentation Details | 00:00:00 | ||
| Tokenization Details | 00:00:00 | ||
| Vocab Cache Details | 00:00:00 | ||
| Advanced DL2J | |||
| Build Locally From Master Details | 00:00:00 | ||
| Contribute to DL4J (Developer Guide) Details | 00:00:00 | ||
| Choose a Neural Net Details | 00:00:00 | ||
| Use the Maven Build Tool Details | 00:00:00 | ||
| Vectorize Data With Canova Details | 00:00:00 | ||
| Build a Data Pipeline Details | 00:00:00 | ||
| Run Benchmarks Details | 00:00:00 | ||
| Configure DL4J in Ivy, Gradle, SBT etc Details | 00:00:00 | ||
| Find a DL4J Class or Method Details | 00:00:00 | ||
| Save and Load Models Details | 00:00:00 | ||
| Interpret Neural Net Output Details | 00:00:00 | ||
| Visualize Data with t-SNE Details | 00:00:00 | ||
| Swap CPUs for GPUs Details | 00:00:00 | ||
| Customize an Image Pipeline Details | 00:00:00 | ||
| Perform Regression With Neural Nets Details | 00:00:00 | ||
| Troubleshoot Training & Select Network Hyperparameters Details | 00:00:00 | ||
| Visualize, Monitor and Debug Network Learning Details | 00:00:00 | ||
| Speed Up Spark With Native Binaries Details | 00:00:00 | ||
| Build a Recommendation Engine With DL4J Details | 00:00:00 | ||
| Use Recurrent Networks in DL4J Details | 00:00:00 | ||
| Build Complex Network Architectures with Computation Graph Details | 00:00:00 | ||
| Train Networks using Early Stopping Details | 00:00:00 | ||
| Download Snapshots With Maven Details | 00:00:00 | ||
| Customize a Loss Function Details | 00:00:00 | ||
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