Illustrating The Landscape And Applications Of Deep Learning

Episode • Oct 22, 2019 • 56m

Summary

Deep learning is a phrase that is used more often as it continues to transform the standard approach to artificial intelligence and machine learning projects. Despite its ubiquity, it is often difficult to get a firm understanding of how it works and how it can be applied to a particular problem. In this episode Jon Krohn, author of Deep Learning Illustrated, shares the general concepts and useful applications of this technique, as well as sharing some of his practical experience in using it for his work. This is definitely a helpful episode for getting a better comprehension of the field of deep learning and when to reach for it in your own projects.

Announcements

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Your host as usual is Tobias Macey and today I’m interviewing Jon Krohn about his recent book, deep learning illustrated

Interview

Introductions
How did you get introduced to Python?
Can you start by giving a brief description of what we’re talking about when we say deep learning and how you got involved with the field?
- How does your background in neuroscience factor into your work on designing and building deep learning models?
What are some of the ways that you leverage deep learning techniques in your work?
What was your motivation for writing a book on the subject?
- How did the idea of including illustrations come about and what benefit do they provide as compared to other books on this topic?
While planning the contents of the book what was your thought process for determining the appropriate level of depth to cover?
- How would you characterize the target audience and what level of familiarity and proficiency in employing deep learning do you wish them to have at the end of the book?
How did you determine what to include and what to leave out of the book?
- The sequencing of the book follows a useful progression from general background to specific uses and problem domains. What were some of the biggest challenges in determining which domains to highlight and how deep in each subtopic to go?
Because of the continually evolving nature of the field of deep learning and the associated tools, how have you guarded against obsolescence in the content and structure of the book?
- Which libraries did you focus on for your examples and what was your selection process?
  - Now that it is published, is there anything that you would have done differently?
One of the critiques of deep learning is that the models are generally single purpose. How much flexibility and code reuse is possible when trying to repurpose one model pipeline for a slightly different dataset or use case?
- I understand that deployment and maintenance of models in production environments is also difficult. What has been your experience in that regard, and what recommendations do you have for practitioners to reduce their complexity?
What is involved in actually creating and using a deep learning model?
- Can you go over the different types of neurons and the decision making that is required when selecting the network topology?
In terms of the actual development process, what are some useful practices for organizing the code and data that goes into a model, given the need for iterative experimentation to achieve desired levels of accuracy?
What is your personal workflow when building and testing a new model for a new use case?
What are some of the limitations of deep learning and cases where you would recommend against using it?
What are you most excited for in the field of deep learning and its applications?
- What are you most concerned by?
Do you have any parting words or closing advice for listeners and potential readers?

Keep In Touch

Website
@jonkrohnlearns on Twitter
jonkrohn on GitHub

Picks

Tobias
- Spurious Correlations
Jon
- Data Elixir Newsletter

Closing Announcements

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