Active Learning is a particular case of supervised machine learning. This method is used to build a high-performance classifier while keeping the size of the training dataset small. The valuable data points are picked out by hand.
We must correctly label the data to get the best results from machine learning. However, this is a long and time-consuming process. It is also a problem when unsupervised or semi-supervised Learning deals with massive data sets. Active Learning with strategies that help developers prioritize the data and choose the most valuable samples to the label to get the most out of the training is the answer. It also promises to reduce the number of samples needed by picking the right ones.
We have compiled a list of online resources where you can study active Learning in depth.
Active Learning – Coursera
In this course, you will learn how to build data pipelines by gathering, cleaning, validating, and assessing datasets; using TensorFlow Extended to perform feature engineering, transformation, and selection to extract the most predictive power from your data; and setting up the data lifecycle by utilizing data lineage and provenance metadata tools and enterprise data schemas to track the evolution of data. Understanding machine learning and deep Learning is essential for a successful AI career, but production engineering knowledge is also necessary. The Machine Learning Engineering for Production course combines the fundamental concepts of machine learning with the functional understanding of contemporary software development and engineering roles. It helps you develop skills that are production-ready.
Active Learning Literature Survey – Burr Settles
This resource gives an overview of active Learning and its research. It includes a discussion of how we can make queries, and a summary of the query strategy frameworks suggested so far. There is also an analysis of the empirical and theoretical evidence for how we can set up well active learning works, a summary of the different ways problems and practical issues, and a discussion of related research topics in machine learning.
Active Learning – Synthesis Lectures on Artificial Intelligence and Machine Learning
This book gives you an overview of active Learning. First, it talks about some of the theoretical foundations of active Learning. Then, it provides an overview of the pros and cons of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities.
Online Active Learning with Expert Advice
Data streams in social media typically arrive at a high rate and volume, making it nearly impossible and prohibitively expensive to label every instance. In this article, the researchers address this issue using active Learning with expert guidance, in which the ground truth of an instance is revealed when requested by the proposed active query strategies.
Active Learning in Recommender Systems – Neil Rubens, Dain Kaplan, and Masashi Sugiyama
In this chapter, the researchers give an overview of Active Learning (AL), discuss general goals and things to think about, and then summarize several common ways of doing things. Next, AL methods are grouped based on how people see their primary purpose or goal. These groups are then broken down into two types, instance-based and model-based, to make them easier to understand. Finally, at the end of the chapter, the researchers describe ways that AL methods could be evaluated and summarise how well they work.
Active learning and transfer learning – Lukas Biewald
In this tutorial titled “Active Learning and Transfer Learning,” Lukas Biewald examines the current state of training data, Active Learning, and Transfer Learning.
Source: indiaai.gov.in
