📖 The Scoop
This updated compendium provides the linear algebra background necessary to understand and develop linear algebra applications in data mining and machine learning.Basic knowledge and advanced new topics (spectral theory, singular values, decomposition techniques for matrices, tensors and multidimensional arrays) are presented together with several applications of linear algebra (k-means clustering, biplots, least square approximations, dimensionality reduction techniques, tensors and multidimensional arrays).The useful reference text includes more than 600 exercises and supplements, many with completed solutions and MATLAB applications.The volume benefits professionals, academics, researchers and graduate students in the fields of pattern recognition/image analysis, AI, machine learning and databases.
Genre: Computers / Data Science / Data Modeling & Design (fancy, right?)
🤖Next read AI recommendation
Greetings, bookworm! I'm Robo Ratel, your AI librarian extraordinaire, ready to uncover literary treasures after your journey through "Linear Algebra Tools For Data Mining (Second Edition)" by Dan A Simovici! 📚✨
Eureka! I've unearthed some literary gems just for you! Scroll down to discover your next favorite read. Happy book hunting! 📖😊
Reading Playlist for Linear Algebra Tools For Data Mining (Second Edition)
Enhance your reading experience with our curated music playlist. It's like a soundtrack for your book adventure! 🎵📚
🎶 A Note About Our Spotify Integration
Hey book lovers! We're working on bringing you the full power of Spotify integration. 🚀 Our application is currently under review by Spotify, so some features might be taking a little nap.
Stay tuned for updates – we'll have those playlists ready for you faster than you can say "plot twist"!
🎲AI Book Insights
Curious about "Linear Algebra Tools For Data Mining (Second Edition)" by Dan A Simovici? Let our AI librarian give you personalized insights! 🔮📚
Book Match Prediction
AI-Generated Summary
Note: This summary is AI-generated and may not capture all nuances of the book.