Hardware Podcasts

252: A Good Heuristic for Pain Tolerance

Embedded.fm · 5 Jul 2018 · 1h 13m · Episode 252

What this episode covers

Katie Malone, a data scientist at Civis Analytics and host of the Linear Digressions podcast, discusses the practical application of machine learning in embedded systems. She explains the distinction between training and inference, highlighting the challenge of integrating black-box neural network outputs into deterministic embedded software. Malone emphasizes that data science is largely about data preparation and problem formulation, not just algorithm selection, and advises engineers to build projects to overcome imposter syndrome.

Most relevant to

early-stage companies

Topics

Mentioned

Companies
Udacity, NVIDIA, CERN
Products
Linear Digressions, AlphaGo
Tools
TensorFlow, Chainer, Coursera

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