Linear Algebra and Learning from Data
Product ID: 100748911
📊Comprehensive linear algebra + deep learning fusion
📚Rigorous yet readable advanced math textbook
🧠Covers neural nets & stochastic gradient descent
CLP 190390
1
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Description
🤖 Elevate your AI game with the ultimate linear algebra & deep learning guide!
- AUTHORED BY A LEGEND - Learn from Prof. Gilbert Strang, a renowned expert in linear algebra education.
- FROM THEORY TO PRACTICE - Includes large matrix computations and compressed sensing for real-world data challenges.
- MASTER THE MATH BEHIND AI - Dive deep into linear algebra fundamentals essential for modern machine learning.
- STAY AHEAD IN DATA SCIENCE - Equip yourself with the mathematical foundation that powers cutting-edge AI models.
- UNLOCK NEURAL NETWORK SECRETS - Explore architectures, backpropagation, and optimization techniques in one volume.
Linear Algebra and Learning from Data by Professor Gilbert Strang is a rigorous yet accessible textbook that uniquely integrates linear algebra with deep learning concepts. Covering fundamental subspaces, matrix computations, neural network architecture, and optimization methods, it is designed for students and professionals aiming to master the mathematical foundations critical to modern AI and data science.
Specifications
| Best Sellers Rank | 524,086 in Books ( See Top 100 in Books ) 59 in Mathematical Modelling 692 in Higher Mathematical Education 939 in Popular Science Maths |
| Customer reviews | 4.6 4.6 out of 5 stars (264) |
| Dimensions | 17.78 x 2.54 x 25.4 cm |
| Edition | First Edition |
| ISBN-10 | 0692196382 |
| ISBN-13 | 978-0692196380 |
| Item weight | 930 g |
| Language | English |
| Print length | 432 pages |
| Publication date | 31 Jan. 2019 |
| Publisher | Cambridge University Press |
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