Exciting New Directions in ML/AI

Over the last few years, there have been several breakthroughs and exciting new research directions in Reinforcement Learning, Hippocampus Inspired Architectures, Attention and Few-Shot Learning. There has been a move towards multi-component, heterogeneous, stateful architectures, many guided by ideas from cognitive sciences. Google DeepMind and Google Brain are leading the… Read More »Exciting New Directions in ML/AI

Convolutional Competitive Learning vs. Sparse Autoencoders (2/2)

This is the second part of our comparison between convolutional competitive learning and convolutional or fully-connected sparse autoencoders. To understand our motivation for this comparison, have a look at the first article. We decided to compare two specific algorithms that tick most of the features we require: K-Sparse autoencoders, and… Read More »Convolutional Competitive Learning vs. Sparse Autoencoders (2/2)
TensorFlow Eager Execution

Eagerly awaiting TensorFlow Eager Execution?

Eager Execution is an imperative, object oriented and more Pythonic way of using TensorFlow. It is a flexible machine learning platform for research and experimentation where operations are immediately evaluated and return concrete values, instead of constructing a computational graph that is executed later.

Datasets for Computer Vision

The dataset is an integral part of an ML engineer’s toolkit. We recently compiled useful information about a range of these well known datasets. It’s all in one place, and hopefully useful to others as well.
Deep Learning Frameworks

Choosing a Machine Learning Framework in 2018

There are plenty of established machine learning frameworks out there, and new frameworks are popping up frequently to address specific niches. We were interested in examining if one of these frameworks fits in our workflow. I surveyed the most popular frameworks, and aim to provide a helpful comparative analysis.