Difference between revisions of "Math for Intelligence"
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* [http://www.3blue1brown.com/ Animated Math | Grant Sanderson @ 3blue1brown.com] | * [http://www.3blue1brown.com/ Animated Math | Grant Sanderson @ 3blue1brown.com] | ||
* [[Courses & Certifications]] | * [[Courses & Certifications]] | ||
| + | ** [http://www.kdnuggets.com/2020/02/free-mathematics-courses-data-science-machine-learning.html Free Mathematics Courses for Data Science & Machine Learning | Matthew Mayo - KDnuggets] | ||
| + | ** [http://developers.google.com/machine-learning/crash-course/prereqs-and-prework Google's Crash Course] | ||
| + | ** [http://bloomberg.github.io/foml/#lectures Bloomberg Lectures] | ||
| + | ** [http://brilliant.org/courses/artificial-neural-networks/ Brilliant.org] | ||
* [http://machinelearningmastery.com/introduction-matrices-machine-learning/ Introduction to Matrices and Matrix Arithmetic for Machine Learning | Jason Brownlee] | * [http://machinelearningmastery.com/introduction-matrices-machine-learning/ Introduction to Matrices and Matrix Arithmetic for Machine Learning | Jason Brownlee] | ||
* [http://www.kdnuggets.com/2018/09/essential-math-data-science.html Essential Math for Data Science: ‘Why’ and ‘How’ | Tirthajyoti Sarkar - KDnuggets] | * [http://www.kdnuggets.com/2018/09/essential-math-data-science.html Essential Math for Data Science: ‘Why’ and ‘How’ | Tirthajyoti Sarkar - KDnuggets] | ||
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* [http://towardsdatascience.com/gentle-dive-into-math-behind-convolutional-neural-networks-79a07dd44cf9 Gentle Dive into Math Behind Convolutional Neural Networks | Piotr Skalski - Towards Data Science] | * [http://towardsdatascience.com/gentle-dive-into-math-behind-convolutional-neural-networks-79a07dd44cf9 Gentle Dive into Math Behind Convolutional Neural Networks | Piotr Skalski - Towards Data Science] | ||
* [http://triseum.com/variant-limits/ Varient: Limits] | * [http://triseum.com/variant-limits/ Varient: Limits] | ||
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* [http://neuralnetworksanddeeplearning.com/index.html Neural Networks and Deep Learning - online book | Michael A. Nielsen] | * [http://neuralnetworksanddeeplearning.com/index.html Neural Networks and Deep Learning - online book | Michael A. Nielsen] | ||
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* [[Convolution vs. Cross-Correlation (Autocorrelation)]] | * [[Convolution vs. Cross-Correlation (Autocorrelation)]] | ||
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* [[Quantum]] algorithms | * [[Quantum]] algorithms | ||
Revision as of 07:00, 8 April 2020
YouTube search... ...Google search
- Statistics for Intelligence
- Finding Paul Revere
- Causation vs. Correlation
- Dot Product
- Animated Math | Grant Sanderson @ 3blue1brown.com
- Courses & Certifications
- Introduction to Matrices and Matrix Arithmetic for Machine Learning | Jason Brownlee
- Essential Math for Data Science: ‘Why’ and ‘How’ | Tirthajyoti Sarkar - KDnuggets
- Gentle Dive into Math Behind Convolutional Neural Networks | Piotr Skalski - Towards Data Science
- Varient: Limits
- Neural Networks and Deep Learning - online book | Michael A. Nielsen
- Convolution vs. Cross-Correlation (Autocorrelation)
- Quantum algorithms
- Fundamentals:
Contents
Getting Started
The Applications of Matrices - What I wish my teachers told me way earlier | MathPrep
- Eigenvalues and eigenvectors | Wikipedia
- Markov Matrix, also known as a stochastic matrix | DeepAI
- Kernels | Wikipedia
- Adjacency matrix | Wikipedia
Mathematics for Machine Learning | M. Deisenroth, A Faisal, and C. Ong .. Companion webpage ...
3blue1brown
Explained
Siraj Raval
Gilbert Strang (MIT) - Linear Algebra
Fourier Transform (FT), Fourier Series, and Fourier Analysis
- Quantum Fourier transform (QFT)
- Engineers solve 50-year-old puzzle in signal processing - Vladimir Sukhoy and Alexander Stoytchev | Mike Krapfl - TechXplore
Joseph Fourier showed that representing a function as a sum of trigonometric functions greatly simplifies the study of heat transfer. Joseph was a French mathematician and physicist born in Auxerre and best known for initiating the investigation of Fourier series, which eventually developed into Fourier analysis and harmonic analysis, and their applications to problems of heat transfer and vibrations. The Fourier transform and Fourier's law of conduction are also named in his honor. Fourier is also generally credited with the discovery of the greenhouse effect.
- Fourier Transform (FT) decomposes a function of time (a signal) into its constituent frequencies. This is similar to the way a musical chord can be expressed in terms of the volumes and frequencies of its constituent notes. Fourier Transform | Wikipedia
- Fourier Series is a periodic function composed of harmonically related sinusoids, combined by a weighted summation. The discrete-time Fourier transform is an example of Fourier series. For functions on unbounded intervals, the analysis and synthesis analogies are Fourier Transform and inverse transform. Fourier Series | Wikipedia
- Fourier Analysis the study of the way general functions may be represented or approximated by sums of simpler trigonometric functions. Fourier Analysis | Wikipedia
