Dot Product - Revision history

BPeat at 18:26, 30 June 2023

2023-06-30T18:26:13Z

BPeat at 18:23, 30 June 2023

2023-06-30T18:23:36Z

BPeat at 15:54, 4 June 2023

2023-06-04T15:54:19Z

BPeat: Text replacement - "http:" to "https:"

2023-03-28T15:04:07Z

Text replacement - "http:" to "https:"

BPeat at 17:40, 19 March 2023

2023-03-19T17:40:04Z

BPeat at 17:39, 19 March 2023

2023-03-19T17:39:38Z

BPeat at 16:08, 22 June 2019

2019-06-22T16:08:54Z

BPeat at 16:08, 22 June 2019

2019-06-22T16:08:08Z

BPeat: Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M..."

2019-06-22T16:06:00Z

Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M..."

New page

{{#seo:
|title=PRIMO.ai
|titlemode=append
|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
}}
[http://www.youtube.com/results?search_query=mathematics+machine+artificial+intelligence+deep+learning+simple YouTube search...]
[http://www.google.com/search?q=mathematics+deep+machine+learning+ML ...Google search]

* [[Causation vs. Correlation]]
* [[Kernel Trick]]
* [http://www.3blue1brown.com/ Animated Math | Grant Sanderson @ 3blue1brown.com]
* [http://developers.google.com/machine-learning/crash-course/prereqs-and-prework Google's Crash Course]
* [http://machinelearningmastery.com/introduction-matrices-machine-learning/ Introduction to Matrices and Matrix Arithmetic for Machine Learning | Jason Brownlee]
* [http://brilliant.org/courses/artificial-neural-networks/ Brilliant.org]
* [http://triseum.com/variant-limits/ Varient: Limits]
* [http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf Probability Cheatsheet]
* [http://neuralnetworksanddeeplearning.com/index.html Neural Networks and Deep Learning - online book | Michael A. Nielsen]
* [http://bloomberg.github.io/foml/#lectures Bloomberg Lectures]
* [http://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about Statistical Learning | T. Hastie, R. Tibshirani - Stanford]
* [http://www.kdnuggets.com/2018/09/essential-math-data-science.html Essential Math for Data Science: ‘Why’ and ‘How’ | Tirthajyoti Sarkar]
* [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://github.com/JonTupitza/Data-Science-On-Ramp/blob/master/00-Fundamental-Statistics.ipynb Fundamental Statistics Jupyter Notebook |] [http://github.com/jontupitza Jon Tupitza]

* Fundamentals:
** [http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/ Linear Algebra | MIT]
** [http://www.khanacademy.org/math/linear-algebra Linear Algebra | Khan Academy]
** [http://www.khanacademy.org/math/statistics-probability Statistics and Probability | Khan Academy]
** [http://www.khanacademy.org/math/differential-calculus Differential Calculus | Khan Academy]
** [http://www.khanacademy.org/math/multivariable-calculus Multivariable Calculus | Khan Academy]

== Getting Started ==
<youtube>V7xvqfIEDXk</youtube>
<youtube>PXwStduNw14</youtube>
<youtube>3icoSeGqQtY</youtube>
<youtube>OmJ-4B-mS-Y</youtube>

== 3blue1brown ==
* [http://www.3blue1brown.com/ Animated Math | Grant Sanderson @ 3blue1brown.com]
** [http://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab Essence of linear algebra]
** [http://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr Essence of calculus]

<youtube>aircAruvnKk</youtube>
<youtube>IHZwWFHWa-w</youtube>
<youtube>Ilg3gGewQ5U</youtube>
<youtube>tIeHLnjs5U8</youtube>

== Explained ==

<youtube>ml4NSzCQobk</youtube>
<youtube>f5liqUk0ZTw</youtube>
<youtube>CliW7kSxxWU</youtube>
<youtube>owuokEE9clQ</youtube>
<youtube>yyNEOwEg6-I</youtube>
<youtube>5MdSE-N0bxs</youtube>
<youtube>kqWCwwyeE6k</youtube>
<youtube>sYlOjyPyX3g</youtube>

=== Dot Product ===

* [[Kernel Trick]]
* [http://en.wikipedia.org/wiki/Dot_product] Dot Product | Wikipedia]

Dot Product =
* Algebraically, the dot product is the sum of the products of the corresponding entries of the two sequences of numbers.
* Geometrically, it is the product of the Euclidean magnitudes of the two vectors and the cosine of the angle between them.

http://ujwlkarn.files.wordpress.com/2016/07/convolution_schematic.gif

[http://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ Take a moment to understand how the computation above is being done. We slide the orange matrix over our original image (green) by 1 pixel (also called ‘stride’) and for every position, we compute element wise multiplication (between the two matrices) and add the multiplication outputs to get the final integer which forms a single element of the output matrix (pink). Note that the 3×3 matrix “sees” only a part of the input image in each stride. In CNN terminology, the 3×3 matrix is called a ‘filter‘ or ‘kernel’ or ‘feature detector’ and the matrix formed by sliding the filter over the image and computing the dot product is called the ‘Convolved Feature’ or ‘Activation Map’ or the ‘Feature Map‘. It is important to note that filters acts as feature detectors from the original input image. | ujjwalkarn]

http://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/Matrix_multiplication_diagram_2.svg/313px-Matrix_multiplication_diagram_2.svg.png
http://betterexplained.com/wp-content/uploads/crossproduct/cross-product-grid.png

== Siraj Raval ==
<youtube>yEUKougrRSk</youtube>
<youtube>MdHtK7CWpCQ</youtube>
<youtube>YzfdL58virc</youtube>
<youtube>xRJCOz3AfYY</youtube>
<youtube>UIFMLK2nj_w</youtube>
<youtube>s0Q3CojqRfM</youtube>
<youtube>8onB7rPG4Pk</youtube>
<youtube>ov_RkIJptwE</youtube>
<youtube>PrkiRVcrxOs</youtube>
<youtube>N4gDikiec8E</youtube>
<youtube>-mu3TYZ_udM</youtube>
<youtube>KYvOIYV_ztE</youtube>
<youtube>-vhYoS3751g</youtube>
<youtube>jPmV3j1dAv4</youtube>

== Josh Starmer - StatQuest ==
* [http://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw StatQuest]

<youtube>yQhTtdq_y9M</youtube>
<youtube>nk2CQITm_eo</youtube>
<youtube>A82brFpdr9g</youtube>
<youtube>pYxNSUDSFH4</youtube>

== Gilbert Strang (MIT) - Linear Algebra ==
* [http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/ MIT Open Courseware - Linear Algebra]
* [http://www.amazon.com/s/ref=nb_sb_noss_2?url=search-alias%3Daps&field-keywords=Gilbert+Strang++Linear+Algebra Gilbert Strang's Books]
* [http://www.youtube.com/watch?v=hNDFwVVKVk0&list=PL221E2BBF13BECF6C YouTube Playlist]

<youtube>hNDFwVVKVk0</youtube>
<youtube>gGYcSjrqbjc</youtube>

== Quantum Algorithm ==
<youtube>LhtnECml-KI</youtube>

== Statistics ==
<youtube>sxQaBpKfDRk</youtube>
<youtube>eDMGDhyDxuY</youtube>
<youtube>dq_D30kyR1A</youtube>
<youtube>pYxNSUDSFH4</youtube>
<youtube>74oUwKezFho</youtube>

@@ Line 20: / Line 20: @@
 [https://www.bing.com/news/search?q=ai+dot+product&qft=interval%3d%228%22 ...Bing News]
-* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] ... [[Causation vs. Correlation]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 * [https://en.wikipedia.org/wiki/Dot_product Dot Product | Wikipedia]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=ai+dot+product YouTube]
@@ Line 11: / Line 20: @@
 [https://www.bing.com/news/search?q=ai+dot+product&qft=interval%3d%228%22 ...Bing News]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] ... [[Causation vs. Correlation]]
 * [https://en.wikipedia.org/wiki/Dot_product Dot Product | Wikipedia]

@@ Line 14: / Line 14: @@
 * [[Causation vs. Correlation]]
 * [[Kernel Trick]]
-* [[Math for Intelligence]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]]
 * [https://en.wikipedia.org/wiki/Dot_product Dot Product | Wikipedia]

@@ Line 15: / Line 15: @@
 * [[Kernel Trick]]
 * [[Math for Intelligence]]
-* [http://en.wikipedia.org/wiki/Dot_product Dot Product | Wikipedia]
+* [https://en.wikipedia.org/wiki/Dot_product Dot Product | Wikipedia]
 Dot Product =
@@ Line 21: / Line 21: @@
 * Geometrically, it is the product of the Euclidean magnitudes of the two vectors and the cosine of the angle between them.
-http://ujwlkarn.files.wordpress.com/2016/07/convolution_schematic.gif
+https://ujwlkarn.files.wordpress.com/2016/07/convolution_schematic.gif
-[http://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ Take a moment to understand how the computation above is being done. We slide the orange matrix over our original image (green) by 1 pixel (also called ‘stride’) and for every position, we compute element wise multiplication (between the two matrices) and add the multiplication outputs to get the final integer which forms a single element of the output matrix (pink). Note that the 3×3 matrix “sees” only a part of the input image in each stride. In CNN terminology, the 3×3 matrix is called a ‘filter‘ or ‘kernel’ or ‘feature detector’ and the matrix formed by sliding the filter over the image and computing the dot product is called the ‘Convolved Feature’ or ‘Activation Map’ or the ‘Feature Map‘. It is important to note that filters acts as feature detectors from the original input image. | ujjwalkarn]
+[https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ Take a moment to understand how the computation above is being done. We slide the orange matrix over our original image (green) by 1 pixel (also called ‘stride’) and for every position, we compute element wise multiplication (between the two matrices) and add the multiplication outputs to get the final integer which forms a single element of the output matrix (pink). Note that the 3×3 matrix “sees” only a part of the input image in each stride. In CNN terminology, the 3×3 matrix is called a ‘filter‘ or ‘kernel’ or ‘feature detector’ and the matrix formed by sliding the filter over the image and computing the dot product is called the ‘Convolved Feature’ or ‘Activation Map’ or the ‘Feature Map‘. It is important to note that filters acts as feature detectors from the original input image. | ujjwalkarn]
-http://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/Matrix_multiplication_diagram_2.svg/313px-Matrix_multiplication_diagram_2.svg.png
+https://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/Matrix_multiplication_diagram_2.svg/313px-Matrix_multiplication_diagram_2.svg.png
-http://betterexplained.com/wp-content/uploads/crossproduct/cross-product-grid.png
+https://betterexplained.com/wp-content/uploads/crossproduct/cross-product-grid.png

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=ai+dot+product YouTube]
 [https://www.quora.com/search?q=ai%20dot%20product ... Quora]

@@ Line 7: / Line 7: @@
 [http://www.youtube.com/results?search_query=dot+product+machine+artificial+intelligence+deep+learning+simple YouTube search...]
 [http://www.google.com/search?q=dot+product+deep+machine+learning+ML ...Google search]
 * [[Causation vs. Correlation]]