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| | |title=PRIMO.ai | | |title=PRIMO.ai |
| | |titlemode=append | | |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 | |
| − | }}
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| − | [http://www.youtube.com/results?search_query=mathematics+machine+artificial+intelligence+deep+learning+simple YouTube search...]
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| − | [http://www.google.com/search?q=mathematics+deep+machine+learning+ML ...Google search]
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| | | | |
| − | * [[Causation vs. Correlation]]
| + | <!-- Google tag (gtag.js) --> |
| − | * [[Kernel Trick]]
| + | <script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script> |
| − | * [http://www.3blue1brown.com/ Animated Math | Grant Sanderson @ 3blue1brown.com]
| + | <script> |
| − | * [http://developers.google.com/machine-learning/crash-course/prereqs-and-prework Google's Crash Course]
| + | window.dataLayer = window.dataLayer || []; |
| − | * [http://machinelearningmastery.com/introduction-matrices-machine-learning/ Introduction to Matrices and Matrix Arithmetic for Machine Learning | Jason Brownlee]
| + | function gtag(){dataLayer.push(arguments);} |
| − | * [http://brilliant.org/courses/artificial-neural-networks/ Brilliant.org]
| + | gtag('js', new Date()); |
| − | * [http://triseum.com/variant-limits/ Varient: Limits]
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| − | * [http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf Probability Cheatsheet]
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| − | * [http://neuralnetworksanddeeplearning.com/index.html Neural Networks and Deep Learning - online book | Michael A. Nielsen]
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| − | * [http://bloomberg.github.io/foml/#lectures Bloomberg Lectures]
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| − | * [http://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about Statistical Learning | T. Hastie, R. Tibshirani - Stanford]
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| − | * [http://www.kdnuggets.com/2018/09/essential-math-data-science.html Essential Math for Data Science: ‘Why’ and ‘How’ | Tirthajyoti Sarkar]
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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]
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| − | * [http://github.com/JonTupitza/Data-Science-On-Ramp/blob/master/00-Fundamental-Statistics.ipynb Fundamental Statistics Jupyter Notebook |] [http://github.com/jontupitza Jon Tupitza]
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| | | | |
| − | * Fundamentals:
| + | gtag('config', 'G-4GCWLBVJ7T'); |
| − | ** [http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/ Linear Algebra | MIT]
| + | </script> |
| − | ** [http://www.khanacademy.org/math/linear-algebra Linear Algebra | Khan Academy]
| + | }} |
| − | ** [http://www.khanacademy.org/math/statistics-probability Statistics and Probability | Khan Academy]
| + | [https://www.youtube.com/results?search_query=ai+dot+product YouTube] |
| − | ** [http://www.khanacademy.org/math/differential-calculus Differential Calculus | Khan Academy]
| + | [https://www.quora.com/search?q=ai%20dot%20product ... Quora] |
| − | ** [http://www.khanacademy.org/math/multivariable-calculus Multivariable Calculus | Khan Academy]
| + | [https://www.google.com/search?q=ai+dot+product ...Google search] |
| − | | + | [https://news.google.com/search?q=ai+dot+product ...Google News] |
| − | == Getting Started ==
| + | [https://www.bing.com/news/search?q=ai+dot+product&qft=interval%3d%228%22 ...Bing News] |
| − | <youtube>V7xvqfIEDXk</youtube>
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| − | <youtube>PXwStduNw14</youtube>
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| − | <youtube>3icoSeGqQtY</youtube>
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| − | <youtube>OmJ-4B-mS-Y</youtube>
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| − | | |
| − | == 3blue1brown ==
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| − | * [http://www.3blue1brown.com/ Animated Math | Grant Sanderson @ 3blue1brown.com]
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| − | ** [http://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab Essence of linear algebra]
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| − | ** [http://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr Essence of calculus]
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| − | | |
| − | <youtube>aircAruvnKk</youtube>
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| − | <youtube>IHZwWFHWa-w</youtube>
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| − | <youtube>Ilg3gGewQ5U</youtube>
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| − | <youtube>tIeHLnjs5U8</youtube>
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| − | | |
| − | == Explained ==
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| − | | |
| − | <youtube>ml4NSzCQobk</youtube>
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| − | <youtube>f5liqUk0ZTw</youtube>
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| − | <youtube>CliW7kSxxWU</youtube>
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| − | <youtube>owuokEE9clQ</youtube>
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| − | <youtube>yyNEOwEg6-I</youtube>
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| − | <youtube>5MdSE-N0bxs</youtube>
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| − | <youtube>kqWCwwyeE6k</youtube>
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| − | <youtube>sYlOjyPyX3g</youtube>
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| − | | |
| − | | |
| − | === Dot Product ===
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| | | | |
| − | * [[Kernel Trick]] | + | * [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] |
| − | * [http://en.wikipedia.org/wiki/Dot_product] Dot Product | Wikipedia] | + | * [https://en.wikipedia.org/wiki/Dot_product Dot Product | Wikipedia] |
| | | | |
| | Dot Product = | | Dot Product = |
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| | * Geometrically, it is the product of the Euclidean magnitudes of the two vectors and the cosine of the angle between them. | | * 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]
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| − | | |
| − | http://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/Matrix_multiplication_diagram_2.svg/313px-Matrix_multiplication_diagram_2.svg.png
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| − | http://betterexplained.com/wp-content/uploads/crossproduct/cross-product-grid.png
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| − | | |
| − | == Siraj Raval ==
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| − | <youtube>yEUKougrRSk</youtube>
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| − | <youtube>MdHtK7CWpCQ</youtube>
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| − | <youtube>YzfdL58virc</youtube>
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| − | <youtube>xRJCOz3AfYY</youtube>
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| − | <youtube>UIFMLK2nj_w</youtube>
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| − | <youtube>s0Q3CojqRfM</youtube>
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| − | <youtube>8onB7rPG4Pk</youtube>
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| − | <youtube>ov_RkIJptwE</youtube>
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| − | <youtube>PrkiRVcrxOs</youtube>
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| − | <youtube>N4gDikiec8E</youtube>
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| − | <youtube>-mu3TYZ_udM</youtube>
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| − | <youtube>KYvOIYV_ztE</youtube>
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| − | <youtube>-vhYoS3751g</youtube>
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| − | <youtube>jPmV3j1dAv4</youtube>
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| − | | |
| − | == Josh Starmer - StatQuest ==
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| − | * [http://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw StatQuest]
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| − | | |
| − | <youtube>yQhTtdq_y9M</youtube>
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| − | <youtube>nk2CQITm_eo</youtube>
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| − | <youtube>A82brFpdr9g</youtube>
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| − | <youtube>pYxNSUDSFH4</youtube>
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| − | | |
| − | == Gilbert Strang (MIT) - Linear Algebra ==
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| − | * [http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/ MIT Open Courseware - Linear Algebra]
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| − | * [http://www.amazon.com/s/ref=nb_sb_noss_2?url=search-alias%3Daps&field-keywords=Gilbert+Strang++Linear+Algebra Gilbert Strang's Books]
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| − | * [http://www.youtube.com/watch?v=hNDFwVVKVk0&list=PL221E2BBF13BECF6C YouTube Playlist]
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| − | | |
| − | <youtube>hNDFwVVKVk0</youtube>
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| − | <youtube>gGYcSjrqbjc</youtube>
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| | | | |
| − | == Quantum Algorithm ==
| + | [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] |
| − | <youtube>LhtnECml-KI</youtube>
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| | | | |
| − | == Statistics ==
| + | https://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/Matrix_multiplication_diagram_2.svg/313px-Matrix_multiplication_diagram_2.svg.png |
| − | <youtube>sxQaBpKfDRk</youtube>
| + | https://betterexplained.com/wp-content/uploads/crossproduct/cross-product-grid.png |
| − | <youtube>eDMGDhyDxuY</youtube>
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| − | <youtube>dq_D30kyR1A</youtube>
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| − | <youtube>pYxNSUDSFH4</youtube>
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| − | <youtube>74oUwKezFho</youtube>
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