7 ms·

Thanks for this. I'm currently re-learning statics/probabilities and linear algebra so your book will be useful in a few months down the line ;)

knoble 11y ago
Would you mind sharing any of the resources you are using for re-learning? I've been meaning to do the same.
- gamapuna 11y ago
  A couple of friends recommended these:- (Not sure if they are relevant though for deep learning specifically) 1) http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/ http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-... 2) https://www.khanacademy.org/math/linear-algebra/vectors_and_spaces https://www.khanacademy.org/math/linear-algebra/vectors_and_... If anyone knows anything else (relevant to deep learning) could you please share :)
- exox 11y ago
  Introduction To Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/ Is an excellent statistical learning reference.
- lindbergh 11y ago
  Just saying, but if you want to hop onto the ML bandwagon (for instance), then don't bother going over linear algebra or probabilities first, and instead just learn what you need as you go. For example, the first sections of this book are already devoted to getting you on the right track, and it's somewhat standard to do so. And besides, there's no need in learning what are rotation matrices if you won't use them.
  - yompers888 11y ago
    As a counterpoint, if parent is interested in taking ML further, a solid foundation in linear algebra will be huge when more advanced signal processing applications come up.
- osoba 11y ago
  For probability/statistics you could also use the MIT Course https://www.edx.org/course/introduction-probability-science-mitx-6-041x-1 https://www.edx.org/course/introduction-probability-science-... Same course if you prefer the classroom lectures http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/video-lectures/ http://ocw.mit.edu/courses/electrical-engineering-and-comput... Or if you want more rigor you can go through these notes that cover the same material but in a more formal way (via sigma algebras and measure theory) http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-436j-fundamentals-of-probability-fall-2008/lecture-notes/ http://ocw.mit.edu/courses/electrical-engineering-and-comput...
- mindcrime 11y ago
  I've been going through this series of video lectures on Youtube: https://www.youtube.com/playlist?list=PL5102DFDC6790F3D0 https://www.youtube.com/playlist?list=PL5102DFDC6790F3D0 for a basic "Stats 101" course. There's also this archived Coursera course. There aren't any active sections to sign up for, but the videos are still available: https://class.coursera.org/introstats-001 https://class.coursera.org/introstats-001
- phatbyte 11y ago
  I'm mostly using Khan Academy at the moment. But I see several people already posted alternatives which is nice to have ;)
- zintinio5 11y ago
  * Foundations of Machine Learning * All of Statistics * Doing Bayesian Data Analysis Also the ML specialization on Coursera
neovive 11y ago
Khan Academy has excellent material covering Probability and Statistics (https://www.khanacademy.org/math/probability https://www.khanacademy.org/math/probability) and Linear Algebra (https://www.khanacademy.org/math/linear-algebra https://www.khanacademy.org/math/linear-algebra).
- phatbyte 11y ago
  This is exactly where I'm learning from as well.
tmsam 11y ago
I am a big fan of Linear Algebra Done Right, if you are looking for an actual, dead-tree book with good explanations.
frigg 11y ago
If you want to learn probability and statistics I recommend http://probabilitycourse.com http://probabilitycourse.com For linear algebra I heard Paul Dawkins' document is great, it's not on his site anymore but you can find it online. I've read calculus 1 and 1/3 of calculus 2 and it's good material.
- phatbyte 11y ago
  This book looks amazing. Thanks for this.
eachro 11y ago
There isn't much hard probability/statistics in deep learning since most of the stuff is empirical(IE: these neural network architectures work b/c we tried it and it works!). I'd say that deep learning is very accessible to the novice practictioner if you really want to dive into it; it doesn't require the same sort of mathematical background that something like signal processing might.

knoble 11y ago

Would you mind sharing any of the resources you are using for re-learning? I've been meaning to do the same.

gamapuna 11y ago
A couple of friends recommended these:- (Not sure if they are relevant though for deep learning specifically) 1) http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/ http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-... 2) https://www.khanacademy.org/math/linear-algebra/vectors_and_spaces https://www.khanacademy.org/math/linear-algebra/vectors_and_... If anyone knows anything else (relevant to deep learning) could you please share :)
exox 11y ago
Introduction To Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/ Is an excellent statistical learning reference.
lindbergh 11y ago
Just saying, but if you want to hop onto the ML bandwagon (for instance), then don't bother going over linear algebra or probabilities first, and instead just learn what you need as you go. For example, the first sections of this book are already devoted to getting you on the right track, and it's somewhat standard to do so. And besides, there's no need in learning what are rotation matrices if you won't use them.
- yompers888 11y ago
  As a counterpoint, if parent is interested in taking ML further, a solid foundation in linear algebra will be huge when more advanced signal processing applications come up.
osoba 11y ago
For probability/statistics you could also use the MIT Course https://www.edx.org/course/introduction-probability-science-mitx-6-041x-1 https://www.edx.org/course/introduction-probability-science-... Same course if you prefer the classroom lectures http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/video-lectures/ http://ocw.mit.edu/courses/electrical-engineering-and-comput... Or if you want more rigor you can go through these notes that cover the same material but in a more formal way (via sigma algebras and measure theory) http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-436j-fundamentals-of-probability-fall-2008/lecture-notes/ http://ocw.mit.edu/courses/electrical-engineering-and-comput...
mindcrime 11y ago
I've been going through this series of video lectures on Youtube: https://www.youtube.com/playlist?list=PL5102DFDC6790F3D0 https://www.youtube.com/playlist?list=PL5102DFDC6790F3D0 for a basic "Stats 101" course. There's also this archived Coursera course. There aren't any active sections to sign up for, but the videos are still available: https://class.coursera.org/introstats-001 https://class.coursera.org/introstats-001
phatbyte 11y ago
I'm mostly using Khan Academy at the moment. But I see several people already posted alternatives which is nice to have ;)
zintinio5 11y ago
* Foundations of Machine Learning * All of Statistics * Doing Bayesian Data Analysis Also the ML specialization on Coursera

neovive 11y ago

Khan Academy has excellent material covering Probability and Statistics (https://www.khanacademy.org/math/probability https://www.khanacademy.org/math/probability) and Linear Algebra (https://www.khanacademy.org/math/linear-algebra https://www.khanacademy.org/math/linear-algebra).

phatbyte 11y ago
This is exactly where I'm learning from as well.

tmsam 11y ago

I am a big fan of Linear Algebra Done Right, if you are looking for an actual, dead-tree book with good explanations.

frigg 11y ago

If you want to learn probability and statistics I recommend http://probabilitycourse.com http://probabilitycourse.com For linear algebra I heard Paul Dawkins' document is great, it's not on his site anymore but you can find it online. I've read calculus 1 and 1/3 of calculus 2 and it's good material.

phatbyte 11y ago
This book looks amazing. Thanks for this.

eachro 11y ago

There isn't much hard probability/statistics in deep learning since most of the stuff is empirical(IE: these neural network architectures work b/c we tried it and it works!). I'd say that deep learning is very accessible to the novice practictioner if you really want to dive into it; it doesn't require the same sort of mathematical background that something like signal processing might.