Linear Algebra and Learning from Data
Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Stats for nerds
5recommend
13mentions
12people
2018–2026years
38% recommend62% neutral
Mentions per year
What Hacker News says
- If you liked 3B1B and prefer intuition/applications-heavy view, then definitely Strang over Axler. Check out especially his newer textbook "Linear Algebra and Learning from Data".
Axler is more of a pure math textbook - if you want to dive more into proofs and abstractions.
- MIT OCW is an amazing resource -- anyone can learn from Strang, which is a goldmine.
He also created a course on using Linear Algebra for machine learning:
> Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.
- MIT OCW Course: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-an...)
- The text book website: Linear Algebra and Learning from Data (2019) https://math.mit.edu/~gs/learningfromdata/
- The Classic Linear Algebra Course: https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010...
- For a really intuitive introduction to linear algebra, I highly recommend Gilbert Strang's "Linear Algebra and Learning from Data": https://math.mit.edu/~gs/learningfromdata/
- Check out "Linear Algebra and Learning from Data" by Gilbert Strang. It includes a nice introduction to Linear Algebra, touches on relevant statistics and optimization, then puts them all together in chapters on neural networks. It's a textbook, so exercises are included.
- Strang just published a new book called "Linear Algebra and Learning From Data" [1] which I only just started but find to be quite enjoyable so far. It's simultaneously conversational but also quite terse (similar to "All of Statistics"). There are many advanced and very contemporary applications covered in the book, with a focus towards machine learning.
[1]: https://www.amazon.com/Linear-Algebra-Learning-Gilbert-Stran...
- "Dive Into Deep Learning" may be good in that it usually has code alongside any mathematical notation: https://d2l.ai/index.html
I have not actually looked at it in detail, but the legendary Gilbert Strang, in addition to his classic linear algebra course, also has a course that aims to teach enough linear algebra to explain deep learning called "Linear Algebra and Learning From Data". Maybe this is also helpful.
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