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http://traffic.libsyn.com/sedaily/TensorFlow_with_Rajat__Edited.mp3Podcast: Play in new window | Download TensorFlow is Google’s open source machine learning library. Rajat Monga is the engineering director for TensorFlow. In this episode, we cover how to use TensorFlow, including an example of how to build a machine learning model to identify whether a picture contains a cat or not. TensorFlow was built with the mission of simplifying the process of deploying a machine learning model from
http://traffic.libsyn.com/sedaily/datavalidation_edited_2.mp3Podcast: Play in new window | Download Data Validation is the process of ensuring that data is accurate. In many software domains, an application is pulling in large quantities of data from external sources. That data will eventually be exposed to users, and it needs to be correct. Radius Intelligence is a company that aggregates data on small businesses. In order to ensure that business addresses and phone numbers are
http://traffic.libsyn.com/sedaily/Xeneta.mp3Podcast: Play in new window | Download Machine learning has become simplified. Similar to how Ruby on Rails made web development approachable, scikit-learn takes away much of the frustrating aspects of machine learning, and lets the developer focus on building functionality with high-level APIs. Per Harald Borgen is a developer at Xeneta. He started programming fairly recently, but has already built a machine learning application that cuts down on
http://traffic.libsyn.com/sedaily/healthcareML_Edited.mp3Podcast: Play in new window | Download “Building a model to predict disease and deploying that in the wild – the bar for success is much higher there than, say, deciding what ad to show you.” Diagnosing illness today requires the trained eye of a doctor. With machine learning, we might someday be able to diagnose illness using only a data set. Today on Software Engineering Daily, we are joined
“I definitely think we can try to abstract away the first principles of intelligence and then try to go from these principles to an intelligent machine that might look nothing like the brain.”
“You’ve got software engineers who are interested in machine learning, and think what they need to do is just bring in another module and then that will solve their problem. It’s particularly important for those people to understand that this is a different type of beast.”
“You don’t mind if failures slow things down, but its very important that failures do not stop forward progress.”
“When I was a graduate student, I was sitting in the office of my advisor in electrical engineering and he said, ‘Look out that window – you see a Volkswagon, I see a realization of a random variable.’ ”
“It’s a great time to be an engineer.”
Information retrieval and search engineering are becoming more intertwined with machine learning and natural language processing, leading to a wealth of work to be done in the field.
“Changing anything changes everything.”
Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems.
Data science competitions are an effective way to crowdsource the best solutions for challenging datasets. Kaggle is a platform for data scientists to collaborate and compete on machine learning problems with the opportunity to win money from the competitions’ sponsors.
Data science is a broad topic with numerous subfields such as data engineering and machine learning. Yad Faeq returns to the podcast to discuss data science at a high level, and rescue Software Engineering Daily from the threat of the hype vortex.
There is a need for more data scientists to make sense of the vast amounts of data we produce and store. Dataquest is an in-browser platform for learning data science that is tackling this problem.
Vik Paruchuri is the founder of Dataquest. He was previously a machine learning engineer at EdX and before that a U.S. diplomat.
Data science is saving and improving lives by leveraging sensor data and machine learning. Pivotal makes software platforms and database products to enable enterprises to make use of their data.
Sarah Aerni is principal data scientist at Pivotal.