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What is TensorFlow?
It would be a challenge nowadays to find a machine learning engineer who has heard nothing about TensorFlow. Initially created by Google Brain team for some internal purposes, such as spam filtering on Gmail, it was open-sourced in 2015 and became the most popular deep learning framework in the... Read more
Automating Machine Learning: Just How Much?
Many businesses are interested in deploying machine learning, predictive analytics, or AI to gain the upper hand and make the right decisions. This can be a struggle as finding the right technology and trustworthy experts is both expensive and complex. Even when the technology is provided, deploying and automating... Read more
Asking the Right Questions with Machine Learning
Chris Gropp, a PhD student at Clemson University, spoke at HPCC Systems Tech Talk 10, focusing on how to plan effectively at the start of a machine learning research project to achieve a successful outcome. This blog shares his experience on how to ask the right questions with machine... Read more
Darwin: Machine Learning Beyond Predefined Recipes
While machine learning has enabled massive advancements across industries, it requires significant development and maintenance efforts from data science teams. The next evolution in human intelligence is automating the creation of machine learning models that do not follow predefined formulas, but rather adapt and evolve according to the problem’s... Read more
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