5 ms·

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

by knoble 10y ago

gamapuna 10y 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 10y 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 10y 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 10y 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 10y 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 10y 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 10y ago
I'm mostly using Khan Academy at the moment. But I see several people already posted alternatives which is nice to have ;)
zintinio5 10y ago
* Foundations of Machine Learning * All of Statistics * Doing Bayesian Data Analysis Also the ML specialization on Coursera

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

by knoble 10y ago

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

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 10y 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 10y 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 10y 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 10y 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 10y 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 10y ago

I'm mostly using Khan Academy at the moment. But I see several people already posted alternatives which is nice to have ;)

zintinio5 10y ago

* Foundations of Machine Learning * All of Statistics * Doing Bayesian Data Analysis Also the ML specialization on Coursera