Exploring The Patterns And Practices For Deep Learning With Andrew Ferlitsch

Summary Deep learning is gaining an immense amount of popularity due to the incredible results that it is able to offer with comparatively little effort. Because of this there are a number of engineers who are trying their hand at building machine learning models with the wealth of frameworks that are available. Andrew Ferlitsch wrote a book to capture the useful patterns and best practices for building models with deep learning to make it more approachable for newcomers ot the field. In this episode he shares his deep expertise and extensive experience in building and teaching machine learning across many companies and industries. This is an entertaining and educational conversation about how to build maintainable models across a variety of applications. Announcements Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. 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Open up your pipeline and see exactly which code strings are broken – so you can fix the issue immediately. Create more reliable data products. Go to pythonpodcast.com/databand today to start your free trial! Your host as usual is Tobias Macey and today I’m interviewing Andrew Ferlitsch about the patterns and practices for deep learning applications Interview Introductions How did you get introduced to Python? Can you start by describing the major elements of a model architecture? What is the relationship between the specific learning task being addressed and the architecture of the learning network? In your experience, what is the level of awareness of a typical ML engineer or data scientist with respect to the most current design patterns in deep learning? Your currently working on a book about deep learning patterns and practices. What was your motivation for starting that project? What are your goals for the book? How have advancements in the operability of machine learning influenced the ways that the models are designed and trained? How do recent approaches such as transfer learning impact the needs of the supporting tools and infrastructure? Can you describe the different design patterns that you cover in your book and the selection process for when and how to apply them? What are the aspects of bringing deep learning to production that continue to be a challenge? What are some of the emerging practices that you are optimistic about? What are some of the industry trends or areas of current research that you are most excited about? What are the most interesting, innovative, or unexpected patterns that you have encountered? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the book? What are some of the other resources that you recommend for listeners to learn more about how to build production ready models? Keep In Touch LinkedIn @AndrewFerlitsch on Twitter andrewferlitsch on GitHub Picks Tobias Designing Data Intensive Applications (affiliate link) Closing Announcements Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at pythonpodcast.com/chat Links Google Cloud AI Sharp Corporation Deep Learning Patterns and Practices (affiliate link) use the code podinit21 at checkout for 35% off all books at Manning! CID Bioscience Latent Space AI Winter Numerical Stability Surrogate Model GAN == Generative Adversarial Network Gradient Descent The Gang of 4 – Design Patterns: Elements of Reusable Object-Oriented Software (affiliate link) The Lottery Hypothesis Manning Publications (affiliate link) The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA