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,… Read More »Convolutional Competitive Learning vs. Sparse Autoencoders (2/2)
Competitive learning is a branch of unsupervised learning that was popular a long, long time ago in the 1990s. Older readers may remember – the days… Read More »Convolutional Competitive Learning vs. Sparse Autoencoders (1/2)
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.
We’ve just uploaded a spin-off research paper to arXiv titled “Sparse Unsupervised Capsules Generalize Better”. So what’s it all about? Capsules Networks You may have… Read More »Sparse Unsupervised Capsules Generalize Better
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.
ML Today Today’s Machine Learning has demonstrated unprecedented performance in what seems like every application thrown at it. Almost all the success has been based… Read More »The case for Episodic Memory in Machine Learning
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.
SVHN is relatively new and popular dataset, a natural next step to MNIST and complement to other popular computer vision datasets. This is an overview of the common preprocessing techniques used and the best performance benchmarks, as well as a look at the state-of-the-art neural network architectures used.
Releasing a set of tools for converting the Street View House Numbers (SVHN) dataset into images with additional preprocessing options such as grayscaling.