Blog

Oct 26, 2015 · post

Hello Deep Learning

image


Deep learning is a hot and fascinating research area, particularly when applied to classifying images. While researching the Fast Forward Labs Deep Learning: Image Analysis report, we played with a lot of very cool technology. In this blog post, we offer a guide to getting started with deep learning by using APIs from some of the most interesting deep-learning-as-a-service startups.

These APIs accept images and/or video, and quickly classify objects, ideas, and items shown in the images and video. We used this capability to build pictograph.us, an app that allows you to visualize your Instagram photos by the contents of the photos.

We have many ideas for other applications, and wanted share some tips and code to help you get started working in this field as well.

If you want to get started with deep learning for both images and videos, check out our repo hello_deep_learning.  There’s a nice jupyter notebook to get you started as quickly as possible.  We chose to focus on MetaMind, Dextro, CloudSight, and Clarifai because their mature and robust APIs handle large numbers of images while maintaining high quality image predictions.

We strongly recommend starting with an API, because it’s still very challenging to install and configure the open source libraries. That said, if you want to dig a bit deeper, the model zoo has many pre-trained models you can load up into caffe.  

Interested in image recognition? Check out the GoogLeNet.  

Want to play with automatic video descriptions? Check out Translating Videos to Natural Language.  

Want to play with face detection? Check out Face CNN descriptor.  

All these models have been pre-trained and simply need to be loaded up into caffe, then you can start throwing data at them!

Finally, if you want to dig even deeper, we recommend keras. It’s a very simple python library to start building your own neural models. But don’t mistake simplicity for lack of power: we’re using it to train new and interesting language models, and the simplicity enables us to focus on the algorithms and techniques.

With these services and libraries, you’ll be able to start identifying objects in images or videos in no time. Have a cool idea to build something using this new capability? Tell us at @fastforwardlabs!

-Micha

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Oct 29, 2015 · interview
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Oct 20, 2015 · post

Latest posts

Nov 15, 2022 · newsletter

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Nov 14, 2022 · post

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Oct 20, 2022 · newsletter

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Sep 21, 2022 · newsletter

CFFL September Newsletter
September 2022 Welcome to the September edition of the Cloudera Fast Forward Labs newsletter. This month we’re talking about ethics and we have all kinds of goodies to share including the final installment of our Text Style Transfer series and a couple of offerings from our newest research engineer. Throw in some choice must-reads and an ASR demo, and you’ve got yourself an action-packed newsletter! New Research! Ethical Considerations When Designing an NLG System In the final post of our blog series on Text Style Transfer, we discuss some ethical considerations when working with natural language generation systems, and describe the design of our prototype application: Exploring Intelligent Writing Assistance.
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Sep 8, 2022 · post

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Reports

In-depth guides to specific machine learning capabilities
FF24

Text Style Transfer

The NLP task of text style transfer (TST) aims to automatically control the style attributes of a piece of text while preserving the content, which is an important consideration for making NLP more user-centric. In this report, we explore text style transfer through an applied use case — neutralizing subjectivity bias in free text. Along the way, we describe our sequence-to-sequence modeling approach leveraging HuggingFace Transformers, and present a set of custom, reference-free evaluation metrics for quantifying model performance. Finally, we conclude with a discussion of ethics centered around our prototype: Exploring Intelligent Writing Assistance.
Read the report  →
FF22

Inferring Concept Drift Without Labeled Data

Concept drift occurs when the statistical properties of a target domain change overtime causing model performance to degrade. Drift detection is generally achieved by monitoring a performance metric of interest and triggering a retraining pipeline when that metric falls below some designated threshold. However, this approach assumes ample labeled data is available at prediction time - an unrealistic constraint for many production systems. In this report, we explore various approaches for dealing with concept drift when labeled data is not readily accessible.
Read the report  →
FF19

Session-based Recommender Systems

Being able to recommend an item of interest to a user (based on their past preferences) is a highly relevant problem in practice. A key trend over the past few years has been session-based recommendation algorithms that provide recommendations solely based on a user’s interactions in an ongoing session, and which do not require the existence of user profiles or their entire historical preferences. This report explores a simple, yet powerful, NLP-based approach (word2vec) to recommend a next item to a user. While NLP-based approaches are generally employed for linguistic tasks, here we exploit them to learn the structure induced by a user’s behavior or an item’s nature.
Read the report  →
FF18

Few-Shot Text Classification

Text classification can be used for sentiment analysis, topic assignment, document identification, article recommendation, and more. While dozens of techniques now exist for this fundamental task, many of them require massive amounts of labeled data in order to be useful. Collecting annotations for your use case is typically one of the most costly parts of any machine learning application. In this report, we explore how latent text embeddings can be used with few (or even zero) training examples and provide insights into best practices for implementing this method.
Read the report  →

Prototypes

Machine learning prototypes and interactive notebooks
Notebook

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!
https://colab.research.google.com/github/fastforwardlabs/whisper-openai/blob/master/WhisperDemo.ipynb
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
Notebooks

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com

Cloudera Fast Forward Labs

Making the recently possible useful.

Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

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