Linear algebra and its applications

What Hacker News says

1. richard_chaseAug 2026
   "Linear Algebra and Its Applications" by David Lay is the best introductory linear algebra textbook, hands down. Love it.
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2. derangedHorseAug 2026
   As someone who's watched all Strang's lectures and read most of Axler's book, I found Linear Algebra and Its Applications to give the most interesting treatment on the topic. What made it so special was its clear explanations and the use cases it introduced.

   The applications not only provided the context to get a deeper understanding of the concepts, it also made the math relevant to the real world. By showing a range of problems, from the chemical stoichiometry to the algorithmic SVD, it inspires the reader to look for more problems that can be solved with the toolset being introduced.

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3. bigger_cheeseOct 2023
   I can second this, in my Engineering degree the Linear Algebra course (and the Calculus course) were both taught by the Math Faculty at my Uni.

   The textbook we used was "Linear Algebra: And its Applications" by David C Lay 20 years later I still keep this textbook with me at my desk and consult it a few times a year when I need to jog my memory on something. I consider it to be a very good textbook even if it doesn't it doesn't contain any rigorous proofs or axioms...

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4. nextosJun 2023
   Also, really important to refer to Boyd's prequel book, which is much simpler and can be read by anyone here without any prior background: https://web.stanford.edu/~boyd/vmls

   It has tons of code and exercises in Julia and Python. Start here, excellent to get a taste of linear algebra and its applications.

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5. FullStackDev1Apr 2023
   I have been a Java Developer then full stack developer all my career. I want to get started on AI. I am not sure where to start as it is overwhelming to look at the rapid changes coming in. Are there a list of books i can start with? My goal is to be not outdated in 6 months and lose my job to chatGPT or some other version of it. So i asked ChatGPT to give me a detailed plan and here is what it gave me. Could you please let me know if this is a way to go or if there is a better way

   Transitioning from a full-stack engineer to the AI field can be a fulfilling and exciting journey. To make the most of this transition, you should focus on building a strong foundation in mathematics, programming, and machine learning concepts. Here's a detailed learning plan, broken down into four stages:

   Stage 1: Building a Strong Foundation

   Mathematics:

   Linear Algebra: "Linear Algebra and Its Applications" by Gilbert Strang

   Calculus: "Calculus: Early Transcendentals" by James Stewart

   Probability & Statistics: "Probability and Statistics for Engineers and Scientists" by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying E. Ye

   Optimization: "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe

   Programming:

   Python: "Python Crash Course" by Eric Matthes

   Git: "Pro Git" by Scott Chacon and Ben Straub

   Machine Learning & Data Science Basics:

   "Introduction to Data Science" by Laura Igual and Santi Seguí "Python Data Science Handbook" by Jake VanderPlas

   Stage 2: Learning Machine Learning and Deep Learning

   Online Courses:

   Coursera: "Machine Learning" by Andrew Ng

   Coursera: "Deep Learning Specialization" by Andrew Ng

   Fast.ai: "Practical Deep Learning for Coders"

   Books:

   "Pattern Recognition and Machine Learning" by Christopher M. Bishop "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

   Stage 3: Expanding Knowledge of AI Subfields

   Natural Language Processing (NLP):

   "Speech and Language Processing" by Daniel Jurafsky and James H. Martin

   "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper

   Computer Vision:

   "Computer Vision: Algorithms and Applications" by Richard Szeliski

   "Deep Learning for Computer Vision" by Adrian Rosebrock Reinforcement Learning:

   "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto

   "Deep Reinforcement Learning Hands-On" by Maxim Lapan

   Stage 4: Staying Current and Gaining Practical Experience

   Read research papers:

   Subscribe to the arXiv mailing list in your areas of interest Regularly read papers from conferences like NeurIPS, ICML, and ACL Participate in online competitions:

   Kaggle: Participate in machine learning competitions to improve your skills and build your portfolio AIcrowd: Another platform for AI competitions Contribute to open-source projects:

   TensorFlow, PyTorch, or other popular AI frameworks Look for projects on GitHub related to your interests and contribute by fixing bugs, implementing new features, or improving documentation Network with AI professionals:

   Attend AI conferences, workshops, and local meetups Join AI-related forums, LinkedIn groups, or online communities like Reddit's r/MachineLearning Remember that the learning plan can be adjusted based on your pace and interests. The key is to stay consistent and always be curious to learn more. Good luck on your journey into the AI field!

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6. williamscalesJan 2023
   The three for me were:

   - Principles of Mathematical Analysis by Walter Rudin (aka “Little Rudin”)

   - Linear Algebra and its Applications by David Strang

   - Elementary Differential Equations and Boundary Value Problems by Boyce and Diprima

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