ICLR 2016 Takeaways: Adversarial Models & Optimization

Posted by & filed under Machine Learning.

Takeaways and favorite papers from ICLR Last week, three members of indico’s Advanced Development team attended the International Conference on Learning Representations (ICLR). ICLR focuses mainly on representation learning — or working with raw data to build better features to solve complex problems. This covers ideas such as deep learning, kernel learning, compositional models, as… Read more

TensorFlow Data Input (Part 1): Placeholders, Protobufs & Queues

Posted by & filed under Machine Learning, Machine Learning Tutorials.

TensorFlow is a great new deep learning framework provided by the team at Google Brain. It supports the symbolic construction of functions (similar to Theano) to perform some computation, generally a neural network based model. Unlike Theano, TensorFlow supports a number of ways to feed data into your machine learning model. The processes of getting… Read more

Getting Started with MXNet

Posted by & filed under Machine Learning, Machine Learning Tutorials.

So many other frameworks exist, why MXNet? MXNet is a modern interpretation and rewrite of a number of ideas being talked about in the deep learning infrastructure. It’s designed from the ground up to work well with multiple GPUs and multiple computers. When doing multi-device work in other frameworks, the end user frequently has to… Read more

Three Thought-Provoking Ideas from SIGGRAPH ’15

Posted by & filed under Data Science, Developers, indico, Machine Learning, Opinion Piece.

Last month Alec Radford and I had the great pleasure of attending the SIGGRAPH 2015 conference in Los Angeles.  If you don’t know about SIGGRAPH, here’s a quick snippet from their website:  “Since its beginning in 1974 as a small group of specialists in a previously unknown discipline, ACM SIGGRAPH has evolved to become an international… Read more

Visualizing with t-SNE

Posted by & filed under Data Science, Developers, Machine Learning.

Data visualization A big part of working with data is getting intuition on what those data show. Staring at raw data points, especially when there are many of them, is almost never the correct way to tackle a problem. Low dimensional data are easy to visually inspect. You can simply pick pairs of dimensions and… Read more