Watch: Unsupervised Feature Learning with Matrix Decomposition
Supervised learning is among the most powerful tools in data science but it requires a training dataset in which one knows the classes of the input features apriori. For example, a classification algorithm learns the identity of animals through training on a dataset of images that are labeled with... Read more
The Promise of Retrofitting: Building Better Models for Natural Language Processing
Editor’s note: Catherine is a speaker for the upcoming ODSC East 2019 this April 30-May 3! Be sure to check out her talk, “Adding Context and Cognition to Modern NLP Techniques.” OpenAI’s Andrej Karpathy famously said, “I don’t have to actually experience crashing my car into a wall a... Read more
An Open Framework for Secure and Private AI
Like any other industry, AI is constrained by the supply chain that feeds it. For AI, that supply chain is made up of data, computers, and talented scientists to build it all. The most limiting of these is data, as the most valuable datasets are private and thus very... Read more
Artificial Intelligence and Machine Learning in Practice: Anomaly Detection in Army ERP Data
Overview Assessing and improving readiness remains a significant priority for the United States Army. With this priority in mind, the Army recently launched a project to enhance its supply chain data environments by leveraging the power of artificial intelligence (AI) and machine learning (ML). The Army’s Logistics Innovation Agency... Read more
Machine Learning and Compression Systems in Communications and Healthcare
Machine learning has all sorts of applications across disciplines. Two important fields using machine learning to solve long-standing issues are communication and healthcare. Dr. Thomas Wiegand, executive director and professor at the Fraunhofer Henrich Hertz Institutionalization, goes over exciting advances made in these disciplines due to machine learning. [Machine... Read more
Learn Interpretability for Data Science
Editor’s note: Rajiv Shaw will be a speaker at ODSC East 2019 this May! Be sure to check out his talk, “Deciphering the Black Box: Latest Tools and Techniques for Interpretability” there. The impact of machine learning has been tremendous, whether it’s measured in dollars (trillions) or human impact... Read more
The Anatomy of K-Means Clustering
Let’s say you want to classify hundreds (or thousands) of documents based on their content and topics, or you wish to group together different images for some reason. Or what’s even more, let’s think you have that same data already classified but you want to challenge that labeling. You... Read more
Techniques to Overcome Data Scarcity
Editor’s note: Attend ODSC East 2019 this April 30 to May 3 in Boston and check out Parinaz’ talk, “Data Efficiency Through Transfer Learning” there! Supervised machine learning models are being used to successfully solve a whole range of business challenges. However, these models are data-hungry and their performance relies... Read more
Feature Selection Using Genetic Algorithms in R
This is a post about feature selection using genetic algorithms in R, in which we will do a quick review about: What are genetic algorithms? GA in ML? What does a solution look like? GA process and its operators The fitness function Genetics Algorithms in R! Try it yourself... Read more
Automated Machine Learning: Myth Versus Reality
Witnessing the data science field’s meteoric rise in demand across pretty much all industries and areas of scientific research, it’s easy to anticipate efforts to create shortcuts to satisfy the need for more data science practitioners. The current trend of automated machine learning is a great case in point.... Read more