Difference between revisions of "Math for Intelligence"
| Line 22: | Line 22: | ||
* [[Convolution vs. Cross-Correlation (Autocorrelation)]] | * [[Convolution vs. Cross-Correlation (Autocorrelation)]] | ||
* [http://bloomberg.github.io/foml/#lectures Bloomberg Lectures] | * [http://bloomberg.github.io/foml/#lectures Bloomberg Lectures] | ||
| + | * [[Quantum-based]] | ||
* Fundamentals: | * Fundamentals: | ||
| Line 46: | Line 47: | ||
== Explained == | == Explained == | ||
| − | |||
<youtube>ml4NSzCQobk</youtube> | <youtube>ml4NSzCQobk</youtube> | ||
<youtube>f5liqUk0ZTw</youtube> | <youtube>f5liqUk0ZTw</youtube> | ||
Revision as of 17:21, 11 September 2019
YouTube search... ...Google search
- Statistics for Intelligence
- Finding Paul Revere
- Causation vs. Correlation
- Dot Product
- Animated Math | Grant Sanderson @ 3blue1brown.com
- Introduction to Matrices and Matrix Arithmetic for Machine Learning | Jason Brownlee
- Essential Math for Data Science: ‘Why’ and ‘How’ | Tirthajyoti Sarkar
- Gentle Dive into Math Behind Convolutional Neural Networks | Piotr Skalski - Towards Data Science
- Varient: Limits
- Google's Crash Course
- Neural Networks and Deep Learning - online book | Michael A. Nielsen
- Brilliant.org
- Convolution vs. Cross-Correlation (Autocorrelation)
- Bloomberg Lectures
- Quantum-based
- Fundamentals:
Contents
Getting Started
3blue1brown
Explained
Siraj Raval
Gilbert Strang (MIT) - Linear Algebra
Quantum Algorithm
- Quantum Computing for English Majors | John Horgan - Scientific American
- Quantum Algorithm Implementations for Beginners | P. Coles, S. Eidenbenz, S. Pakin, A. Adedoyin, J. Ambrosiano, P. Anisimov, W. Casper, G. Chennupati, C. Coffrin, H. Djidjev, D. Gunter, S. Karra, N. Lemons, S. Lin, A. Lokhov, A. Malyzhenkov, D. Mascarenas, S. Mniszewski, B. Nadiga, D. O'Malley, D. Oyen, L. Prasad, R. Roberts, P. Romero, N. Santhi, N. Sinitsyn, P. Swart, M. Vuffray, J. Wendelberger, B. Yoon, R. Zamora, and W. Zhu
