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Fast Forward Labs research now available without a subscription

Moving forward, all new reports will be publicly available and free to download. In addition, we will be providing access to updated versions of older reports over time, so check back often to explore available free research.

Free research reports

Explore our latest research reports and prototypes, freely accessible to all. 

Interpretability: 2020 Edition

Interpretability, or the ability to explain why and how a system makes a decision, can help us improve models, satisfy regulations, and build better products. Black-box techniques like deep learning have delivered breakthrough capabilities at the cost of interpretability. In this report, recently updated to include techniques like SHAP, we show how to make models interpretable without sacrificing their capabilities or accuracy.

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Deep Learning for Anomaly Detection

From fraud detection to flagging abnormalities in imaging data, there are countless applications for automatic identification of abnormal data. This process can be challenging, especially when working with large, complex data. This report explores deep learning approaches (sequence models, VAEs, GANs) for anomaly detection, when to use them, performance benchmarks, and product possibilities.

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Fast Forward Labs Deep Learning for Image Analysis - 2019 Edition report preview

Subscription-only reports

Updated versions of older reports will be available for free in the future, so check back often.

Fast Forward Labs Transfer Learning for NLP report preview

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.

Fast Forward Labs Deep Learning for Image Analysis - 2019 Edition 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.

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.

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.

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.

Interpretability

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

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.

Summarization

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

Deep learning: Image analysis

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

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.

Natural language generation

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

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