Getting Started with ‘Street View House Numbers’ (SVHN) Dataset

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…

Unsupervised Learning with Spike-Timing Dependent Plasticity

Our brain is a source of great inspiration for the development of Artificial General Intelligence. In fact, one of the common views is that any effort in developing human-level AI is almost destined to fail without an…

New approaches to Deep Networks:

Capsules (Hinton), HTM (Numenta), Sparsey (Neurithmic Systems) and RCN (Vicarious)

Optimization using Adam on Sparse Tensors

Adaptive optimization methods, such as Adam and Adagrad, maintain some statistics over…

Literature Review: ‘A Distributional Perspective on Reinforcement Learning’

This article assesses the research paper, ‘A Distributional Perspective on Reinforcement Learning’ by the authors, Marc G. Bellemare, Will Dabney and Remi Munos, published in the proceedings of the…

AHA! an ‘Artificial Hippocampal Algorithm’ for Episodic Machine Learning

Convolutional Competitive Learning vs. Sparse Autoencoders (1/2)

Survey of Machine Learning Frameworks

Choosing an ML Framework in 2018

Attention in Artificial Intelligence systems

One of the features of our brain is its modularity. It is characterised by distinct but interacting subsystems that underlie key functions such as memory, language, perceptions, etc. Understanding the complex interplay between these modules requires…