Category Machine Learning

Hedge Fund Artificial Intelligence with Xander Dunn

http://traffic.libsyn.com/sedaily/numerai_edited.mp3Podcast: Play in new window | Download A hedge fund is a collection of investors that make bets on the future. The “hedge” refers to the fact that the investors often try to diversify their strategies so that the direction of their bets are less correlated, and they can be successful in a variety of future scenarios. Engineering-focused hedge funds have used what might be called “machine learning” for a

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Multiagent Systems with Peter Stone

http://traffic.libsyn.com/sedaily/multiagent-systems_edited_1.mp3Podcast: Play in new window | Download Multiagent systems involve the interaction of autonomous agents that may be acting independently or in collaboration with each other. Examples of these systems include financial markets, robot soccer matches, and automated warehouses. Today’s guest Peter Stone is a professor of computer science who specializies in multiagent systems and robotics. In this episode, we discuss some of the canonical problems of multiagent systems, which

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Biological Machine Learning with Jason Knight

http://traffic.libsyn.com/sedaily/biodeeplearing_edited.mp3Podcast: Play in new window | Download Biology research is complex. The sample size of a biological data set is often too small to make confident judgments about the biological system being studied. During Jason Knight’s PhD research, the RNA sequence data that he was studying was not significant enough to make strong conclusions about the gene regulatory networks he was trying to understand. After working in academia, and then

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Stripe Machine Learning with Michael Manapat

http://traffic.libsyn.com/sedaily/stripeantifraud_edited.mp3Podcast: Play in new window | Download Every company that deals with payments deals with fraud. The question is not whether fraud will occur on your system, but rather how much of it you can detect and prevent. If a payments company flags too many transactions as fraudulent, then real transactions might accidentally get flagged as well. But if you don’t reject enough of the fraudulent transactions, you might not

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Machine Learning is Hard with Zayd Enam

http://traffic.libsyn.com/sedaily/WhyMLisHard.mp3Podcast: Play in new window | Download Machine learning frameworks like Torch and TensorFlow have made the job of a machine learning engineer much easier. But machine learning is still hard. Debugging a machine learning model is a slow, messy process. A bug in a machine learning model does not always mean a complete failure. Your model could continue to deliver usable results even in the presence of a mistaken

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Deep Learning with Adam Gibson

http://traffic.libsyn.com/sedaily/DeepLearning.mp3Podcast: Play in new window | Download Deep learning uses neural networks to identify patterns. Neural networks allow us to sequence “layers” of computing, with each layer using learning algorithms such as unsupervised learning, supervised learning, and reinforcement learning. Deep learning has taken off in the last few years, but it has been around for much longer. Adam Gibson founded Skymind, the company behind Deeplearning4j. Deeplearning4j is a distributed deep

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Go Data Science with Daniel Whitenack

http://traffic.libsyn.com/sedaily/Go_Data_Science.mp3Podcast: Play in new window | Download Data science is typically done by engineers writing code in Python, R, or another scripting language. Lots of engineers know these languages, and their ecosystems have great library support. But these languages have some issues around deployment, reproducibility, and other areas. The programming language Golang presents an appealing alternative for data scientists. Daniel Whitenack transitioned from doing most of his data science work

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Translation with Vasco Pedro

http://traffic.libsyn.com/sedaily/Unbabel_edited.mp3Podcast: Play in new window | Download Translation is a classic problem in computer science. How do you translate a sentence from one human language into another? This seems like a problem that computers are well-suited to solve. Languages follow well-defined rules, we have lots of sample data to train our machine learning models. And yet, the problem has not been solved–largely because languages don’t always follow rules. We have

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Medical Machine Learning with Razik Yousfi and Leo Grady

http://traffic.libsyn.com/sedaily/heartflow_edited_fixed.mp3Podcast: Play in new window | Download Medical imaging is used to understand what is going on inside the human body and prescribe treatment. With new image processing and machine learning techniques, the traditional medical imaging techniques such as CT scans can be enriched to get a more sophisticated diagnosis. HeartFlow uses data from a standard CT scan to model a human heart and understand blockages of blood flow using

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Python Data Visualization with Jake VanderPlas

http://traffic.libsyn.com/sedaily/python_dataviz_edited_fixed.mp3Podcast: Play in new window | Download Data visualization tools are required to translate the findings of data scientists into charts, graphs, and pictures. Understanding how to utilize these tools and display data is necessary for a data scientist to communicate with people in other domains. In this episode, Srini Kadamati hosts a discussion with Jake VanderPlas about the Python ecosystem for data science and the different attempts at creating

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PANCAKE STACK Data Engineering with Chris Fregly

http://traffic.libsyn.com/sedaily/pancakestack_edited_fixed.mp3Podcast: Play in new window | Download Data engineering is the software engineering that enables data scientists to work effectively. In today’s episode, we explore the different sides of data engineering–the data science algorithms that need to be processed and the implementation of software architectures that enable those algorithms to run smoothly. The PANCAKE STACK is a 12-letter acronym that Chris Fregly gave to a collection of data engineering technologies

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Scikit-learn with Andreas Mueller

http://traffic.libsyn.com/sedaily/scikit_learn-edited.mp3Podcast: Play in new window | Download Scikit-learn is a set of machine learning tools in Python that provides easy-to-use interfaces for building predictive models. In a previous episode with Per Harald Borgen about Machine Learning For Sales, he illustrated how easy it is to get up and running and productive with scikit-learn, even if you are not a machine learning expert. Srini Kadamati hosts today’s show and interviews Andreas

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Music Deep Learning with Feynman Liang

http://traffic.libsyn.com/sedaily/Bachbot_Edited.mp3Podcast: Play in new window | Download Machine learning can be used to generate music. In the case of Feynman Liang’s research project BachBot, the machine learning model is seeded with the music of famous composer Bach. The music that BachBot creates sounds remarkably similar to Bach, although it has been generated by an algorithm, not by a human.   BachBot is a research project on computational creativity. Feynman Liang

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Automated Content with Robbie Allen

http://traffic.libsyn.com/sedaily/wordsmith_edited.mp3Podcast: Play in new window | Download You have probably read a news article that was written by a machine. When earnings reports come out, or a series of sports events like the Olympics occurs, there are so many small stories that need to be written that a news organization like the Associated Press would have to use all of its resources to write enough content to cover it all.

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Artificial Intelligence with Oren Etzioni

http://traffic.libsyn.com/sedaily/AI_Research_Edited_2.mp3Podcast: Play in new window | Download Research in artificial intelligence takes place mostly at universities and large corporations, but both of these types of institutions have constraints that cause the research to proceed a certain way. In a university, basic research might be hindered by lack of funding. At a big corporation, the researcher might be encouraged to study a domain that is not squarely in the interest of

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TensorFlow in Practice with Rajat Monga

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

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Data Validation with Dan Morris

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

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Machine Learning for Sales with Per Harald Borgen

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

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Phone Spam with Truecaller CTO Umut Alp

http://traffic.libsyn.com/sedaily/Truecaller_Edited.mp3Podcast: Play in new window | Download The war against spam has been going on for decades. Email spam blockers and ad blockers help protect us from unwanted messages in our communication and browsing experience. These spam prevention tools are powered by machine learning, which catches most of the emails and ads that we don’t want to see. TrueCaller is a company that is bringing this quality of spam detection

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Machine Learning in Healthcare with David Kale

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

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