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| Oppenheimer reading list book 8. Full list at www.amazon.com/shop/welchlabs @WelchLabsVideo | Uploaded 1 year ago | Updated 1 hour ago
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Oppenheimer reading list book 8. Full list at www.amazon.com/shop/welchlabs @WelchLabsVideo
![Learning to See [Part 7: There is no f]](https://i.ytimg.com/vi/klWUOO4sHaA/hqdefault.jpg "Learning to See [Part 7: There is no f]
In this series, well explore the complex landscape of machine learning and artificial intelligence through one example from the field of computer vision: using a decision tree to count the number of fingers in an image. Its gonna be crazy.
Supporting Code: https://github.com/stephencwelch/LearningToSee
welchlabs.com
@welchlabs")
![Waffles And Harmonic Motion [Part 1]](https://i.ytimg.com/vi/nKavBl1EWiI/hqdefault.jpg "Waffles And Harmonic Motion [Part 1]
In this two part series, we dig into understanding and quantifying simple harmonic motion (SHM). We try to figure our why oscillating systems that move the way they do, and what ideas from physics govern this motion.
I have unapologetically stolen much of my approach from Richard Feynmans wonderful physics lectures: http://www.feynmanlectures.caltech.edu/I_toc.html. The approach presented here was largely borrowed from Feynman chapters 9 and 21. I encourage anyone interesting in going to deeper to read Feynmans lectures.
Supporting code: http://nbviewer.ipython.org/github/stephencwelch/Acoustics-To-Deep-Learning/blob/master/Waffles%20and%20Harmonic%20Motion%20%5BPart%20I%5D.ipynb
It can be forked on github: https://github.com/stephencwelch/Acoustics-To-Deep-Learning
For more on this, see www.welchlabs.com/blog
@stephencwelch")
![Neural Networks Demystified [Part 5: Numerical Gradient Checking]](https://i.ytimg.com/vi/pHMzNW8Agq4/hqdefault.jpg "Neural Networks Demystified [Part 5: Numerical Gradient Checking]
When building complex systems like neural networks, checking portions of your work can save hours of headache. Here well check our gradient computations.
Supporting code:
https://github.com/stephencwelch/Neural-Networks-Demystified
Link to excellent Stanford tutorial: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
In this series, we will build and train a complete Artificial Neural Network in python. New videos every other friday.
Part 1: Data + Architecture
Part 2: Forward Propagation
Part 3: Gradient Descent
Part 4: Backpropagation
Part 5: Numerical Gradient Checking
Part 6: Training
Part 7: Overfitting, Testing, and Regularization
@stephencwelch")
![Imaginary Numbers Are Real [Part 10: Complex Functions]](https://i.ytimg.com/vi/pNp8Qf20-sA/hqdefault.jpg "Imaginary Numbers Are Real [Part 10: Complex Functions]
Supporting Code: https://github.com/stephencwelch/Imaginary-Numbers-Are-Real
More information and resources: http://www.welchlabs.com
Imaginary numbers are not some wild invention, they are the deep and natural result of extending our number system. Imaginary numbers are all about the discovery of numbers existing not in one dimension along the number line, but in full two dimensional space. Accepting this not only gives us more rich and complete mathematics, but also unlocks a ridiculous amount of very real, very tangible problems in science and engineering.
Part 1: Introduction
Part 2: A Little History
Part 3: Cardans Problem
Part 4: Bombellis Solution
Part 5: Numbers are Two Dimensional
Part 6: The Complex Plane
Part 7: Complex Multiplication
Part 8: Math Wizardry
Part 9: Closure
Part 10: Complex Functions
Part 11: Wandering in Four Dimensions
Part 12: Riemanns Solution
Part 13: Riemann Surfaces
Want to learn more or teach this series? Check out the Imaginary Numbers are Real Workbook: http://www.welchlabs.com/resources.")
![How to Science [Part 5: Mathematics]](https://i.ytimg.com/vi/sTSNTWKGkbw/hqdefault.jpg "How to Science [Part 5: Mathematics]
pdf: http://www.welchlabs.com/guides
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https://www.premiumbeat.com/royalty-free-tracks/out-of-the-wood
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