Let's learn about some key math concepts behind deep learning shall we? We'll build a 3 layer neural network and dive into some key concepts that makes deep learning give us such incredible results.
Coding challenge for this video:
https://github.com/llSourcell/how_to_do_math_for_deep_learning
Jovian's Winning Code:
https://github.com/jovianlin/siraj-intro-to-DL-03/blob/master/Siraj%2003%20Challenge.ipynb
Vishal's Runner up Code:
https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Sentiment_Analysis
Linear Algebra cheatsheet:
http://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf
Calculus cheatsheet:
http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf
Statistics cheatsheet:
http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf
And if you have never had experience with any of these 3 and want to learn from absolute scratch, I'd recommend the respective KhanAcademy courses:
https://www.khanacademy.org/math
More Learning Resources:
https://people.ucsc.edu/~praman1/static/pub/math-for-ml.pdf
http://www.vision.jhu.edu/tutorials/ICCV15-Tutorial-Math-Deep-Learning-Intro-Rene-Joan.pdf
http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/
Join us in our Slack channel:
http://wizards.herokuapp.com/
And Part I of this book is so dope, seriously:
http://www.deeplearningbook.org/
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