Blog

Dec 21, 2015 · post

Five Good Books* of 2015

* Plus a few papers, videos, and other fun things.

We love books, and it feels fitting to end 2015 with a round-up of a few that we thought were particularly excellent this year. 

Our favorite book for data science beginners: Data Science from Scratch: First Principles with Python by Joel Grus (and if you order from O’Reilly directly, use discount code PCBW and get 50% off most ebooks/videos and 40% off most print books).

Our favorite machine learning resources for developers and data scientists: There wasn’t one machine learning book we loved more than these papers and videos: Yoav Goldberg’s Primer on Neural Network Models for Natural Language Processing (PDF), Yann LeCun, Yoshua Bengio & Geoffrey Hinton’s paper on Deep Learning, Chris Olah’s neural network blog and finally, this YouTube channel with two minute paper descriptions and Tom Scott’s video Automated Weapons and the Battlefield of 2050. And if you’re a Python developer, we liked Fluent Python for brushing up on idiomatic Python 3.

Science fiction that helps us imagine the future: We loved Neal Stephenson’s Seveneves for the near-future space hacking and Ann Leckie’s Ancillary Justice for the unusual artificially intelligence characters in a far-future society (the third book in the trilogy was published this year). For a far far future space criminal caper with lots of imaginative future technology, give Hannu Rajaniemi’s The Quantum Thief a try.

Resources for managing data projects: We recommend this paper on Technical Debt in Machine Learning Systems (PDF) for some perspective on the long-term maintenance of machine learning models.

This is really not a book: …but we love the Talking Machines podcast, with lively conversation with guests from across the machine learning community.

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Jan 12, 2016 · announcement
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Dec 15, 2015 · post

Latest posts

Nov 15, 2022 · newsletter

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Oct 20, 2022 · newsletter

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Sep 21, 2022 · newsletter

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by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books.
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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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