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Machine Learning Challenges You Might Not See Coming
There seems to be a skills gap, and a skills misunderstanding, when it comes to Data Science, Engineering, and DevOps as a joint process. At our machine learning consultancy, Infinia ML, we view deployment as a sequential process across teams: (1) Data Science explores data and develops algorithm(s). (2a)... Read more
Using NLP and ML to Analyze Legislative Burdens Upon Businesses
The process of legal reasoning and decision making is heavily reliant on information stored in text. Tasks like due diligence, contract review, and legal discovery, that are traditionally time-consuming, can be automated, saving a huge amount of time. This makes the development of approaches that leverage natural language processing... Read more
Why Do Tree Ensembles Work?
Ensembles of decision trees (e.g., the random forest and AdaBoost algorithms) are powerful and well-known methods of classification and regression. We will survey work aimed at understanding the statistical properties of decision tree ensembles, with the goal of explaining why they work. An elementary probabilistic motivation for ensemble methods... Read more
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
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