indico Blog

Resources for exploring machine learning and data science

October 4, 2016

Posted by
indico

TensorFlow in Practice (Video + Slides)

TensorFlow is a wonderful tool for rapidly implementing neural networks. Nathan Lintz teaches the basics of TensorFlow and how to build neural networks in just a few lines of code at the Boston Machine Learning Meetup.

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.

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