ML with Dale Markowitz: GCPPodcast 194
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Google Cloud Platform1.17 млн
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02.10.19 – 2 5744:02Популярные
104 дня – 36023:50
104 дня – 45017:18Опубликовано 1 октября 2019, 17:08
Original post → goo.gle/2mlJ0Jn
On the podcast this week, we have a great interview with Google Developer Advocate, Dale Markowitz. Aja Hammerly and Jon Foust are your hosts, as we talk about machine learning, its best use cases, and how developers can break into machine learning and data science. Dale talks about natural language processing as well, explaining that it’s basically the intersection of machine learning and text processing. It can be used for anything from aggregating and sorting Twitter posts about your company to sentiment analysis.
For developers looking to enter the machine learning space, Dale suggests starting with non life-threatening applications, such as labeling pictures. Next, consider the possible mistakes the application can make ahead of time to help mitigate issues. To help prevent the introduction of bias into the model, Dale suggests introducing it to as many different types of project-appropriate data sets as possible. It’s also important to continually monitor your model.
Later in the show, we talk Google shop, learning about all the new features in Google Translate and AutoML.
For more GCP Podcasts → goo.gle/30x1aYn
Subscribe to the Google Cloud Platform channel → goo.gle/GCP
On the podcast this week, we have a great interview with Google Developer Advocate, Dale Markowitz. Aja Hammerly and Jon Foust are your hosts, as we talk about machine learning, its best use cases, and how developers can break into machine learning and data science. Dale talks about natural language processing as well, explaining that it’s basically the intersection of machine learning and text processing. It can be used for anything from aggregating and sorting Twitter posts about your company to sentiment analysis.
For developers looking to enter the machine learning space, Dale suggests starting with non life-threatening applications, such as labeling pictures. Next, consider the possible mistakes the application can make ahead of time to help mitigate issues. To help prevent the introduction of bias into the model, Dale suggests introducing it to as many different types of project-appropriate data sets as possible. It’s also important to continually monitor your model.
Later in the show, we talk Google shop, learning about all the new features in Google Translate and AutoML.
For more GCP Podcasts → goo.gle/30x1aYn
Subscribe to the Google Cloud Platform channel → goo.gle/GCP
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