Deep Learning on the JVM - DZone Big Data

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DL4J is a pretty awesome open source project that works with Spark and Hadoop. Deep Learning 4J also works as a YARN app! It includes Text, NLP, Canova Vectorization Lib for ML, Scientific computing for the JVM, distributed with clusters, and works with CUDA GPU kernels. DL4J is used for anomaly detection (fraud detection), recommender systems, predictive analytics with logs and image recognition. In a related open source project, Skymind built a numerical computing library ND4J, or n-dimensional arrays for Java, essentially porting Numpy to the JVM.


Intro to Machine Learning in H2O

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The focus of this workshop is machine learning using the H2O R and Python packages. H2O is an open source distributed machine learning platform designed for big data, with the added benefit that it's easy to use on a laptop (in addition to a multi-node Hadoop or Spark cluster). The core machine learning algorithms of H2O are implemented in high-performance Java; however, fully featured APIs are available in R, Python, Scala, REST/JSON and also through a web interface. Since H2O's algorithm implementations are distributed, this allows the software to scale to very large datasets that may not fit into RAM on a single machine. H2O currently features distributed implementations of generalized linear models, gradient boosting machines, random forest, deep neural nets, dimensionality reduction methods (PCA, GLRM), clustering algorithms (K-means), and anomaly detection methods, among others.


Fast and Scalable Machine Learning in R and Python with H2O

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The focus of this talk is scalable machine learning using the H2O R and Python packages. H2O is an open source distributed machine learning platform designed for big data, with the added benefit that it's easy to use on a laptop (in addition to a multi-node Hadoop or Spark cluster). The core machine learning algorithms of H2O are implemented in high-performance Java; however, fully featured APIs are available in R, Python, Scala, REST/JSON and also through a web interface. Since H2O's algorithm implementations are distributed, this allows the software to scale to very large datasets that may not fit into RAM on a single machine. H2O currently features distributed implementations of generalized linear models, gradient boosting machines, random forest, deep neural nets, dimensionality reduction methods (PCA, GLRM), clustering algorithms (K-means), and anomaly detection methods, among others.


Deep Learning Prerequisites: Linear Regression in Python

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I am a data scientist, big data engineer, and full stack software engineer. For my masters thesis I worked on brain-computer interfaces using near-infrared spectroscopy. These assist non-verbal and non-mobile persons communicate with their family and caregivers. I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.


Google Cloud: Build your own machine learning-powered robot arm using TensorFlow and Google Cloud

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Specifically, you can tell the robot what flavor you like, such as "chewy candy," "sweet chocolate" or "hard mint." The robot then processes your instructions via voice recognition and natural language processing, recommends a particular kind of candy and uses image recognition to recognize and select that recommendation. The entire demo is powered by deep-learning technology running on Cloud Machine Learning Engine (the fully-managed TensorFlow runtime from Google Cloud) and Cloud machine learning APIs. This demo is intended to serve as a microcosm of a real-world machine learning (ML) solution. For example, Kewpie, a major food manufacturer in Japan, used the same Google Cloud technology to build a successful Proof of Concept (PoC) for doing anomaly detection for diced potato in a factory.