Linear Algebra and Learning from Data
Product ID: 100748911
📈Covers advanced matrix computations & optimization
📊Comprehensive linear algebra + deep learning fusion
🤖In-depth neural nets & backpropagation insights
RM798
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Description
🤓 Elevate your AI game with the ultimate linear algebra + deep learning guide!
- RIGOROUS YET READABLE - A textbook that balances academic depth with accessible explanations for serious learners.
- FROM THEORY TO PRACTICE - Explore singular value decomposition, compressed sensing, and large matrix techniques powering data science.
- 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 toolkit that top-tier ML professionals rely on.
- UNLOCK NEURAL NETWORK SECRETS - Understand architectures, stochastic gradient descent, and backpropagation with clarity.
Linear Algebra and Learning from Data by Professor Gilbert Strang uniquely integrates linear algebra with deep learning concepts, offering a rigorous yet accessible course covering fundamental subspaces, matrix computations, probability, optimization, and neural network architectures. Ideal for advanced students and professionals aiming to deepen their understanding of the mathematics behind machine learning.
Specifications
| Best Sellers Rank | 540,477 in Books ( See Top 100 in Books ) 48 in Mathematical Modelling 679 in Higher Mathematical Education 1,011 in Popular Science Maths |
| Customer reviews | 4.6 4.6 out of 5 stars (263) |
| Dimensions | 19.61 x 2.49 x 24.21 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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