Coursera, Udacity, Stanford, Berkeley, and Google offer excellent intros to Machine Learning. Here are a full set of notes, and primers to get started.
Deep Learning/resources 2019. 8. 18. 21:35Highly recommend Google ML introduction
Coursera, Udacity, Stanford, Berkeley, and Google offer excellent intros to Machine Learning. Here are a full set of notes, and primers to get started.
— Courses:
https://developers.google.com/machine-learning/crash-course/ml-intro
https://onlinehub.stanford.edu/
— Review:
https://www.ubuntupit.com/best-machine-learning-courses-for-free/#20-best-machine-learning-course
Grant Sanderson, Stanford, ML Introductions, are an excellent place to start, before taking online classes. Also take a look at the AI 101 CheatSheet, below:
https://m.youtube.com/watch?feature=youtu.be&v=IHZwWFHWa-w
— Primers, Notes, Groups:
AI 101 CheatSheet: http://www.montreal.ai/ai4all.pdf
Facebook Group: https://www.facebook.com/groups/MontrealAI/
Curated Notes: http://www.academy.montreal.ai/
Jonathan Hui. Hui has excellent primers on Deep Learning that I’ve enjoyed: https://medium.com/@jonathan_hui
— Mathematics:
‘The Matrix Calculus You Need For Deep Learning’ — explained.ai
Terence Parr and Jeremy Howard
https://explained.ai/matrix-calculus/index.html ; http://www.matrixcalculus.org :: https://en.m.wikipedia.org/wiki/Matrix_calculus
— https://machinelearningmastery.com/introduction-to-eigendecomposition-eigenvalues-and-eigenvectors/
Ref — Deep Learning (Adaptive Computation and Machine Learning series)
Ian Goodfellow, Yoshua Bengio, Aaron Courville, pg. 42, https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/
—•
Primer on Deep Learning, Grant Sanderson (B.S. Mathematics, Stanford '15).
Grant Sanderson became one of the most popular tutors at Khan Academy, before launching 3blue1brown. Grant animates his tutorials with a self-made Python library; here, he introduces neural networks: https://www.3blue1brown.com/neural-networks
detail: https://m.youtube.com/watch?v=tIeHLnjs5U8&t=0m40s
Backgrounders:
https://en.m.wikipedia.org/wiki/3Blue1Brown
https://stanfordirl.com/grant-sanderson-bs15
https://www.numberphile.com/podcast/3blue1brown
[Q&A with Grant Sanderson (3blue1brown): https://m.youtube.com/watch?v=Qe6o9j4IjTo] cc : Pavel Grinfeld]
#machinelearning #tensorflow #pytorch #artificialintelligence #datascience
Coursera, Udacity, Stanford, Berkeley, and Google offer excellent intros to Machine Learning. Here are a full set of notes, and primers to get started.
— Courses:
https://developers.google.com/machine-learning/crash-course/ml-intro
https://onlinehub.stanford.edu/
— Review:
https://www.ubuntupit.com/best-machine-learning-courses-for-free/#20-best-machine-learning-course
Grant Sanderson, Stanford, ML Introductions, are an excellent place to start, before taking online classes. Also take a look at the AI 101 CheatSheet, below:
https://m.youtube.com/watch?feature=youtu.be&v=IHZwWFHWa-w
— Primers, Notes, Groups:
AI 101 CheatSheet: http://www.montreal.ai/ai4all.pdf
Facebook Group: https://www.facebook.com/groups/MontrealAI/
Curated Notes: http://www.academy.montreal.ai/
Jonathan Hui. Hui has excellent primers on Deep Learning that I’ve enjoyed: https://medium.com/@jonathan_hui
— Mathematics:
‘The Matrix Calculus You Need For Deep Learning’ — explained.ai
Terence Parr and Jeremy Howard
https://explained.ai/matrix-calculus/index.html ; http://www.matrixcalculus.org :: https://en.m.wikipedia.org/wiki/Matrix_calculus
— https://machinelearningmastery.com/introduction-to-eigendecomposition-eigenvalues-and-eigenvectors/
Ref — Deep Learning (Adaptive Computation and Machine Learning series)
Ian Goodfellow, Yoshua Bengio, Aaron Courville, pg. 42, https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/
—•
Primer on Deep Learning, Grant Sanderson (B.S. Mathematics, Stanford '15).
Grant Sanderson became one of the most popular tutors at Khan Academy, before launching 3blue1brown. Grant animates his tutorials with a self-made Python library; here, he introduces neural networks: https://www.3blue1brown.com/neural-networks
detail: https://m.youtube.com/watch?v=tIeHLnjs5U8&t=0m40s
Backgrounders:
https://en.m.wikipedia.org/wiki/3Blue1Brown
https://stanfordirl.com/grant-sanderson-bs15
https://www.numberphile.com/podcast/3blue1brown
[Q&A with Grant Sanderson (3blue1brown): https://m.youtube.com/watch?v=Qe6o9j4IjTo] cc : Pavel Grinfeld]
#machinelearning #tensorflow #pytorch #artificialintelligence #datascience
