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      Enterprise Data Cloud

      Enterprise-class security and governance. Multi-function data analytics. An elastic cloud experience. No silos. No lock-in. Ever.

      • Industrialize AI
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      • Hortonworks Data Platform
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    Fast Forward Labs research previews

    Preview all research reports and prototypes

    Our research focuses on emerging data and machine learning-enabled trends. Accompanying the reports are working prototypes that exhibit the capabilities of the algorithm and offer detailed technical advice on its practical application.

    Transfer Learning for NLP

    Natural language processing (NLP) technologies can translate language, answer questions, and generate human-like text, but the underlying deep learning techniques require costly datasets, infrastructure, and expertise. In this report, we show how to use transfer learning to adapt existing models to any NLP application, making it easier to build high-performance NLP systems.

    Explore the prototype

    Fast Forward Labs Transfer Learning for NLP report preview

    Deep Learning for Image Analysis - 2019 Edition

    Convolutional neural networks (CNNs or ConvNets) excel at learning meaningful representations of features and concepts within images, making CNNs valuable for solving problems in multiple domains, from medical imaging to manufacturing. In this report, we show how to select the right deep learning models for image analysis tasks and techniques for debugging deep learning models.

    Explore the prototype

    Fast Forward Labs Deep Learning for Image Analysis - 2019 Edition report preview
    Cloudera Fast Forward Labs: Learning with limited labeled data report preview

    Learning with Limited Labeled Data

    In this report, we focus on learning with limited labeled data, a collaborative approach between human and machines that reduces the amount of labeled data typically required by supervised machine learning yet performs comparably.

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    Explore the prototype

    Federated learning

    In this report, we focus on federated learning, an approach for training machine learning models on distributed edge node data while ensuring privacy and minimizing communication costs.

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    Explore the prototype
    Image of Multi-Task Learning Report and Prototype

    Multi-task learning

    In this report, we focus on multi-task learning, a new approach to machine learning that allows algorithms to master tasks in parallel.

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    Preview the prototype

    Semantic recommendations

    In this report, we show how using the semantic content of items can help solve common recommendation pitfalls such as the cold start problem, and open up new product possibilities.

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    Preview the prototype

    Interpretability

    In this report, we show how to make models interpretable without sacrificing their capabilities or accuracy.

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    Probabilistic programming

    Here, we show how to use probabilistic programming and Bayesian inference to easily build tools that make better predictions for more effective decision making.

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    Summarization

    Learn how to use deep learning and embeddings to make text computable for a variety of business applications and products.

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    Preview the prototype

    Deep learning: Image analysis

    This report explores the history and current state of deep learning, explains how to apply it, and predicts future developments.

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    Probabilistic methods for realtime streams

    Here, we explore probabilistic methods that offer highly efficient models for extracting value from streams of data as they are generated.

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    Natural language generation

    In this report, we look at how machine systems can turn highly structured data into human language narrative.

    Preview the report
    Preview the prototype

    Read the Fast Forward Labs blog

     

    • Automating Weak Supervision
      What is weak supervision?...
    • Theories of (machine) learning should shape horizon scanning - not just application
      And no, not this kind of horizon… (image credit)...

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