indico Blog

Resources for exploring machine learning and data science

August 4, 2016

Posted by
Amanda Sivaraj

Deep Learning in Fashion (Part 2): Matching Recommendations

We partnered with DeepLearning.TV to introduce the concept of transfer learning and show how we used it to build a fashion matching demo with our Custom Collections API. Follow along with us in this series of three posts. In Part 2, we take a look at how e-commerce fashion sites can improve product recommendations to provide a better experience for shoppers, as well as boost likelihood to purchase.

August 2, 2016

Posted by
Amanda Sivaraj

Deep Learning in Fashion (Part 1): Transfer Learning

We partnered with DeepLearning.TV to introduce the concept of transfer learning and show how we used it to build a fashion matching demo with our Custom Collections API. Follow along with us in this series of three posts. In Part 1, we take a closer look at how transfer learning works, and why you can't use it with traditional machine learning algorithms.

July 28, 2016

Posted by
indico

SAS: The Unreasonable Benefits of Deep Learning

Earlier this month, Dan Kuster gave a talk discussing why businesses should consider adopting deep learning solutions. Key takeaways include simplicity, accuracy, flexibility, and some hacks for working with the tech.

July 13, 2016

Posted by
Dan Kuster

Semi-supervised Feature Transfer: The Practical Benefit of Deep Learning Today?

In this case study, we evaluate four different strategies for solving a problem with machine learning. In terms of both technical performance and practical factors like economics and amount of training data required, customized models built from semi-supervised "deep" features using transfer learning outperform models built from scratch, and rival state-of-the-art methods.

June 6, 2016

Posted by
indico

How Machine Learning is Shaping Digital Marketing

Last week Dan Kuster held a workshop at General Assembly Boston on how machine learning is changing -- and improving -- the way digital marketers do their jobs. Video and slides available for those of you who missed it.

May 16, 2016

Posted by
Luke Metz

ICLR 2016 Takeaways: Adversarial Models & Optimization

At the beginning of the month, three members of our Advanced Development team attended the International Conference on Learning Representations in Puerto Rico. Luke discusses some key takeaways and his favorite papers.

May 9, 2016

Posted by
Dan Kuster

The Good, Bad, & Ugly of TensorFlow

Much has changed since we last evaluated TensorFlow back in November. We've been using the framework in daily research and engineering - here's an update on what's happened since.

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Recent Posts

Exploiting Text Embeddings for Industry Contexts

From Synonyms to Object Properties It’s well known that word embeddings are excellent for finding similarities between words --... more

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Making Deep Learning Practical with Smaller Datasets

At the recent VB Summit in Berkeley, Jeff Dean, Head of Google Brain discussed a popular challenge in making Deep Learning a practical... more

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Building Better Search

In speech and writing, how often do we use one term -- and only that term -- to describe an idea? For example, if you were searching... more

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