Published Feb 24, 2017

[MINI] Automated Feature Engineering

Kyle Polich delves into automated feature engineering, drawing insightful parallels between business hierarchies and deep learning processes, while introducing the engaging format of the "Data Skeptic" podcast that makes complex AI topics accessible and enjoyable.

Episode Highlights

Topics covered

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Episode Highlights

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Dexa/Kyle Polich

[MINI] Automated Feature Engineering

Topics covered

Popular Clips

Reporting Hierarchies

Understanding Neural Networks

Skeptical Thinking

Perception and Recognition

Feature Recognition Insights

Deep Learning Insights

Episode Highlights

Feature Recognition

Automated Feature Engineering

Podcast Format Introduction

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[MINI] Automated Feature Engineering

Topics covered

Popular Clips

Episode Highlights

Feature RecognitionThe episode explores the analogy between business structures and deep learning processes, focusing on automated feature engineering. Kyle Polich discusses how neural networks detect edges and build hierarchical features, drawing parallels to organizational reporting systems.

Feature Recognition

Automated Feature EngineeringAutomated feature engineering in deep learning automates the abstraction of features across layers, akin to a business hierarchy. This process is crucial for practical applications like fraud detection, where identifying patterns is key.

Automated Feature Engineering

Podcast Format IntroductionKyle Polich introduces the "Data Skeptic" podcast, which blends expert interviews with engaging discussions on data science and AI. The show's unique format and host dynamics make complex topics accessible and entertaining.

Podcast Format Introduction

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