Explore key concepts of linear algebra in machine learning through this informative 38-minute video lecture. Gain insights from Tai-Danae Bradley, a Postdoc at X, the Moonshot Factory, as she delves into data representations, vector embeddings, and dimensionality reduction. Learn how these fundamental ideas apply to machine learning contexts, with practical examples and clear explanations. Enhance your understanding of linear algebra's role in ML, from basic data representation techniques to advanced dimensionality reduction methods. Benefit from additional resources provided, including Google's ML Crash Course on Collaborative Filtering and recommended readings on eigenvectors and linear algebra. Perfect for those seeking a friendly yet comprehensive introduction to the intersection of linear algebra and machine learning.
Overview
Syllabus
- Introduction
- Data Representations
- Vector Embeddings
- Dimensionality Reduction
- Conclusion
Taught by
TensorFlow
