Jackson Monroe writes:

When humans perform a task, such as playing a game, they selectively pay attention to certain parts of the visual input, gathering relevant information and sequentially combining it to build a representation from the sensory data. In this work, we explore leveraging where humans look in an image as an implicit indication of what is salient for decision making. We build on top of the UNREAL architecture in DeepMind Lab’s 3D navigation maze environment. We train the agent both with original images and foveated images, which were generated by overlaying the original images with saliency maps generated using a real-time spectral residual technique. We investigate the effectiveness of this approach in transfer learning by measuring performance in the context of noise in the environment. Attend Before you Act: Leveraging human visual attention for continual learning

Measuring Information Flow Between Time Series with Shannon and Renyi Transfer Entropy (RTransferEntropy)Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) <doi:10.1515/snde-2012-0044 …

What is Imbalanced Dataset ?The dataset may contain uneven samples /instances , so that it makes the algorithm to predict with accuracy of 1.0 each time u run the model. For example, if u have simple dataset with 4 features and output(target) feature with 2 class, then total no. of instances/samples be 100. Now, out of 100, 80 instances belongs to category1 of the output(target) feature and only 20 instances contribute to the category2 of the output(target) feature. So, obviously, this makes bias in training and predicting the model. So, this dataset refers to Imbalanced dataset.

(This letter, which is a brief summary of this article I published in my Spanish blog, was published on RISKS, Volume 30, Issue 78.)

PABED A Tool for Big Education Data Analysis

Introduction

This post is authored by Chenhui Hu, Data Scientist at Microsoft.

Supervised learning needs labels, or annotations, that tell the algorithm what the right answers are in the training phases of your project. In fact, many of the examples of using MXNet, TensorFlow, and PyTorch start with annotated data sets you can use to explore the various features of those frameworks. Unfortunately, when you move from the examples to application, it’s much less common to have a fully annotated set of data at your fingertips. This tutorial will show you how you can use Amazon Mechanical Turk (MTurk) from within your Amazon SageMaker notebook to get annotations for your data set and use them for training.

Avery writes: